Cost and Quality in Health Care
Eveteg
ContentServer1.pdf-Applicationsofartificialneuralnetworkdsinahealthcareorganzationaldecision-making.pdf
RESEARCH ARTICLE
Applications of artificial neural networks in
health care organizational decision-making: A
scoping review
Nida ShahidID 1,2*, Tim Rappon1, Whitney Berta1
1 Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada, 2 Toronto
Health Economics and Technology Assessment (THETA) Collaborative, University Health Network, Toronto,
Canada
Abstract
Health care organizations are leveraging machine-learning techniques, such as artificial
neural networks (ANN), to improve delivery of care at a reduced cost. Applications of ANN
to diagnosis are well-known; however, ANN are increasingly used to inform health care
management decisions. We provide a seminal review of the applications of ANN to health
care organizational decision-making. We screened 3,397 articles from six databases with
coverage of Health Administration, Computer Science and Business Administration. We
extracted study characteristics, aim, methodology and context (including level of analysis)
from 80 articles meeting inclusion criteria. Articles were published from 1997–2018 and orig-
inated from 24 countries, with a plurality of papers (26 articles) published by authors from
the United States. Types of ANN used included ANN (36 articles), feed-forward networks
(25 articles), or hybrid models (23 articles); reported accuracy varied from 50% to 100%.
The majority of ANN informed decision-making at the micro level (61 articles), between
patients and health care providers. Fewer ANN were deployed for intra-organizational
(meso- level, 29 articles) and system, policy or inter-organizational (macro- level, 10 arti-
cles) decision-making. Our review identifies key characteristics and drivers for market
uptake of ANN for health care organizational decision-making to guide further adoption of
this technique.
Introduction
As health care systems in developed countries transform towards a value based, patient-cen-
tered model of care delivery, we face new complexities relating to improving the structure and
management of health care delivery; for example, improving integration of processes in care
delivery for patient-centered chronic disease management [1]. Artificial intelligence lies at the
nexus of new technologies with the potential to deliver health care that is cost-effective and
appropriate care in real-time, manage effective and efficient communication among multidis-
ciplinary stakeholders, and address non-traditional care settings, the evolving heathcare work-
place and workforce, and the advent of new and disparate health information systems. With
PLOS ONE | https://doi.org/10.1371/journal.pone.0212356 February 19, 2019 1 / 22
a1111111111
a1111111111
a1111111111
a1111111111
a1111111111
OPEN ACCESS
Citation: Shahid N, Rappon T, Berta W (2019)
Applications of artificial neural networks in health
care organizational decision-making: A scoping
review. PLoS ONE 14(2): e0212356. https://doi.
org/10.1371/journal.pone.0212356
Editor: Olalekan Uthman, The University of
Warwick, UNITED KINGDOM
Received: October 4, 2018
Accepted: January 31, 2019
Published: February 19, 2019
Copyright: © 2019 Shahid et al. This is an open access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the manuscript and its Supporting
Information files.
Funding: The authors received no specific funding
for this work.
Competing interests: The authors have declared
that no competing interests exist.
the rapid uptake of artificial intelligence to make increasingly complex decisions across differ-
ent industries, there are a multitude of solutions capable of addressing these health care man-
agement challenges; however, there is a paucity of guidance on selecting appropriate methods
tailored to the health care industry[2].
Global health care expenditure is expected to reach $8.7 trillion by 2020, driven by aging
populations growing in size and disease complexity, advancements made in medical treat-
ments, rising labour costs and the market expansion of the health care industry. Many health
systems are reported to struggle with updating aging infrastructure and legacy technologies
with already limited capital resources. In an effort toward moving to value-based care, deci-
sion-makers are reported to be strategically shifting the focus to understanding and better
alignment of financial incentives for health care providers in order to bear financial risk; popu-
lation health management including analyses of trends in health, quality and cost; and adop-
tion of innovative delivery models for improved processes and coordination of care.
Health care organizations are required to be increasingly strategic in their management due
to a variety of system interdependences such as emerging environmental demands and com-
peting priorities, that can complicate decision-making process [3]. According to economy the-
ory, most organizations are risk-aversive [4] and decision-makers in health care can face issues
related to culture, technology and risk when making high-risk decisions without the certainty
of high-return [4, 5]. Patient care and operations management requires the interaction of mul-
tiple stakeholders, for example clinicians, front-line/middle managers, senior level executives
to make decisions on a clinical (e.g. diagnosis, treatment and therapy, medication prescription
and administration), and non-clinical (e.g. budget, resource allocation, technology acquisition,
service additions/reductions, strategic planning) [6].
A white paper published by IBM suggests that with increasing capture and digitization of
health care data (e.g. electronic medical records and DNA sequences), health care organizations
are taking advantage of analyzing large sets of routinely collected digital information in order to
improve service and reduce costs [7]. Reported examples include analyzing clinical, financial and
operational data to answer questions related to effectiveness of programs, making predictions
regarding at-risk patients. The global market for health care predictive analytics is projected was
valued at USD 1.48 billion in 2015 and expected to grow at a rate of 29.3% (compound annual
growth rate) by 2025 [8]. Similarly, global revenue of $811 million is expected to increase 40%
(Compound Annual Growth Rate) by 2021 due the artificial intelligence (AI) market for health
care applications. A subfield of AI, machine learning-as-a-service-market (MLaaS), is expected to
reach $5.4 billion by 2022, with the health care sector as a notable key driver [9].
A recent survey of AI applications in health care reported uses in major disease areas such
as cancer or cardiology and artificial neural networks (ANN) as a common machine learning
technique [10]. Applications of ANN in health care include clinical diagnosis, prediction of
cancer, speech recognition, prediction of length of stay [11], image analysis and interpretation
[12] (e.g. automated electrocardiographic (ECG) interpretation used to diagnose myocardial
infarction [13]), and drug development[12]. Non-clinical applications have included improve-
ment of health care organizational management [14], prediction of key indicators such as cost
or facility utilization [15]. ANN has been used as part of decision support models to provide
health care providers and the health care system with cost-effective solutions to time and
resource management [16].
Rationale
Despite its many applications and, more recently, its prominence [17], there is a lack of coher-
ence regarding ANN’s applications and potential to inform decision making at different levels
Applications of ANN in health care organizational decision-making: A scoping review
PLOS ONE | https://doi.org/10.1371/journal.pone.0212356 February 19, 2019 2 / 22
in health care organizations. This review is motivated by a need for a broad understanding the
various applications of ANN in health care and aids researchers interested in bridging the dis-
ciplines of organizational behaviour and computer science. Considering the sheer abundance
in reported use and complexity of the area, it can be challenging to remain abreast of the new
advancements and trends in applications of ANN [18]. Adopters of ANN or researchers new
to the field of AI may find the scope and esoteric terminology of neural computing particularly
challenging [18]. Literature suggests that current reviews on applications of ANN are limited
in scope and generally focus on a specific disease [19] or a particular type of neural network
[20], or they are too broad (i.e. data mining or AI techniques that can include ANN but do not
offer insights specific to ANN) [10]. The overarching goal of this scoping review is to provide a
much-needed comprehensive review of the various applications of ANN in health care organi-
zational decision-making at the micro-, meso-, and macro-levels. The levels pertain to deci-
sions made on the (micro) level of individual patients, or on a (meso) group level (e.g.
departmental or organizational level) where patient preference may be important but not
essential; and on a wider (macro) level by large groups or public organizations related to allo-
cation or utilization of resources where decisions are based on public interest and reflective of
society as a whole [21]. By means of this review, we will identify the nature and extent of rele-
vant literature and describe methodologies and context used.
Overview
According to an overview by Kononenko (2001), as a sub-field of AI, machine learning pro-
vides indispensable tools for intelligent data analysis. Three major branches of machine learn-
ing have emerged since electronic computers came in to use during the 1950s and 1960s:
statistical methods, symbolic learning and neural networks [22]. ANN have been successfully
used to solve highly complex problems within the physical sciences and as of late by scholars
in organizational research as digital tools enabling faster processes of data collection and pro-
cessing [23]. As practical and flexible modelling tools, ANN have an ability to generalize pat-
tern information to new data, tolerate noisy inputs, and produce reliable and reasonable
estimates [23]. ANN belong to a wide class of flexible nonlinear regression and discriminant
models, data reduction models, and nonlinear dynamical systems [24]. ANN are similar to sta-
tistical techniques including generalized linear models, nonparametric regression and discrim-
inant analysis, or cluster analysis [24]. As a statistical model, it’s general composition is one
made of simple, interconnected processing elements that are configured through iterative
exposure to sample data [23]. Its application is particularly valuable under one or more of sev-
eral conditions: when sample data show complex interaction effects or do not meet parametric
assumptions, when the relationship between independent and dependent variables is not
strong, when there is a large unexplained variance in information, or in situations where the
theoretical basis of prediction is poorly understood [23]. ANN architectures are commonly
classified as feed-forward neural networks (e.g. single-layer perceptron, multi-layer percep-
tron, radial basis function networks) or feed-back, or otherwise referred to as recurrent neural
networks (e.g. Competitive networks, Kohonen’s self-organizing maps, Hopfield networks)
[25].
Artificial neural networks
Originally developed as mathematical theories of the information-processing activity of bio-
logical nerve cells, the structural elements used to describe an ANN are conceptually analogous
to those used in neuroscience, despite it belonging to a class of statistical procedures [23].
Applications of ANN in health care organizational decision-making: A scoping review
PLOS ONE | https://doi.org/10.1371/journal.pone.0212356 February 19, 2019 3 / 22
Basics
ANN can have single or multiple layers [23], and consist of processing units (nodes or neu-
rons) that are interconnected by a set of adjustable weights that allows signals to travel through
the network in parallel and consecutively[13, 26]. Generally ANN can be divided in to three
layers of neurons: input (receives information), hidden (responsible for extracting patterns,
perform most of internal processing), and output (produces and presents final network out-
puts) [27].
A review by Agatonovic-Kustrin & Beresford (2000) describes neural computation to be
powered from the connection of its neurons and that each neuron has a weighted input, trans-
fer function and a single output. The authors state that the neuron is activated by the weighed
sum of inputs it receives and the activation signal passes through a transfer function to pro-
duce a single output. The transfer functions, the learning rule and the architecture determine
the overall behaviour of the neural network [26].
Architecture
Sharma & Chopra (2013) describe the two most common types of neural networks applied in
management sciences to be the feed-forward and recurrent neural networks (Fig 1) in compar-
ison with feed-forward networks common to medical applications [28, 29]. A feed-forward
network can be single-layered (e.g. Perceptron, ADALINE) or multi-layered (e.g. Multilayer
Perceptron, Radial Basis Function) [27, 30]. Sharma & Chopra (2013) describe information
flow in feed-forward networks to be unidirectional from input layer, through hidden layers to
the output layer, without any feedback. Whereas, a recurrent or feedback network involves
Fig 1. Conceptual model of a feed-forward and recurrent neural network.
https://doi.org/10.1371/journal.pone.0212356.g001
Applications of ANN in health care organizational decision-making: A scoping review
PLOS ONE | https://doi.org/10.1371/journal.pone.0212356 February 19, 2019 4 / 22
dynamic information processing having at least one feedback loop, using outputs as feedback
inputs (e.g. Hopfield) [27, 30]. Fig 1 illustrates the two types of networks with three layers
(input, hidden and output).
Learning
In an overview of basic concepts, Agatonovic-Kustrin & Beresford (2000) describe ANN
gather knowledge by detecting patterns and relationships in data and “learn” through experi-
ence. The authors state an artificial neural network learns by optimizing its inner unit connec-
tions in order to minimize errors in the predictions that it makes and to reach a desired level
of accuracy. New information can be inputted into the model once the model has been trained
and tested [26]. Also referred to as the generalized delta rule, backpropagation refers to how an
ANN is trained or ‘learns’ based on data. It uses an iterative process involving six steps: (i) sin-
gle case data is passed to input later, output is passed to the hidden layer and multiplied by the
first set of connection weights; (ii) incoming signals are summed, transformed to output and
passed to second connection weight matrix; (iii) incoming signals are summed, transformed
and network output is produced; (iv) output value is subtracted from known value for that
case, error term is passed backward through network; (v) connection weights are adjusted in
proportion to their error contribution; (vi) modified connection weights saved for next cycle,
next case input set queued for next cycle [23]. Sharma & Chopra (2013) broadly classify train-
ing or ‘learning’ methods in ANN into three types: supervised, unsupervised and reinforced
learning. In supervised learning, every input pattern used to train the network is associated
with an output pattern. The error in computed and desired outputs can be used to improve
model performance. In unsupervised learning, the network learns without knowledge of
desired output and by discovering and adapting to features of the input patterns. In reinforce-
ment learning, the network is provided with feedback on if computation performance without
presenting the desired output [30].
Artificial neural networks and regression models
Neural networks are similar to linear regression models in their nature and use. They are com-
prised of input (independent or predictor variable) and output (dependent or outcome vari-
able) nodes, use connection weights (regression coefficients), bias weight (intercept
parameters) and cross-entropy (maximum likelihood estimation) to learn or train (parameter
estimation) a model [31]. ANN learn to perform tasks by using inductive learning algorithms
requiring massive data sets [18]. A working paper on the use of ANN in decision support sys-
tems states that the structure, quality and quantity of data used is critical for the learning pro-
cess and that the chosen attributes must be complete, relevant, measurable and independent
[18]. The authors further observe that in business applications, external data sources (e.g.
industry and trade databases) are typically used to supplement internal data sources.
Classification and prediction modelling
In the book entitled ‘Data Mining: Concepts and Techniques’, classification is defined as the
process of finding a model that describes and distinguishes data classes or concepts based on
analysis of a set of training data [32]. The authors write that models called classifiers predict
categorical class labels and can be used to predict the class label of objects for which the class label is unknown. Furthermore, the process is described to consist of a learning step (when a
classification model is constructed) and a classification step (when a model is used to predict
class labels for a given data). Methods include naïve Bayesian classification, support vector machines, and k-nearest-neighbour classification [32]. Han et al. (2012) suggest that
Applications of ANN in health care organizational decision-making: A scoping review
PLOS ONE | https://doi.org/10.1371/journal.pone.0212356 February 19, 2019 5 / 22
applications can broadly include fraud detection, target marketing, performance prediction,
manufacturing and medical diagnosis.
The available data is divided into two sets for cross-validation: a training set used to develop
a model and a test set, used to evaluate the model’s performance [33, 34]. Appropriate data
splitting is a technique commonly used in machine learning in order to minimize poor gener-
alization (also referred to as over-training or over-fitting) of models [34]. Using more training
data improves the classification model, whereas using more test data contributes to estimating
error accurately [35]. Although a 70:30 ratio can typically be used for training/testing size [36],
various statistical sampling techniques ranging from simple (e.g. simple random sampling,
trial-and-error) to more deterministic (e.g. CADEX, DUPLEX) can be used to split the data
depending on the goals and complexity of the problem [34].
Han and colleagues (2012) write that where classification predicts categorical labels, regres-
sion is used to predict missing or unavailable numerical data values (rather than discrete class labels). The authors describe regression analysis as a statistical methodology often used for
numeric prediction and encompasses identification of distribution trends based on available
data. An example of numeric prediction is when a model is constructed to predict a continu-
ous-valued function or ordered value (as opposed to a class label). Such a model is called a pre-
dictor model and typically uses regression analysis [32].
ANN can be used to perform nonlinear statistical modeling and provide new alternatives to
logistic regression, the most commonly used method for developing predictive models for
dichotomous outcomes in medicine [31]. Users require less formal statistical training and the
networks are able to detect complex non-linear relationships and interactions between depen-
dent and independent variables. ANN can combine and incorporate literature-based and exper-
imental data to solve problems [26]. Other advantages of ANN, relative to traditional predictive
modeling techniques, include fast and simple operation due to compact representation of
knowledge (e.g., weight and threshold value matrices), the ability to operate with noisy or miss-
ing information and generalize to similar unseen data, the ability to learn inductively from
training data and process non-linear functionality critical to dealing with real-word data [37].
Although ANN do not require knowledge of data source, they require large training sets due
to the numerous estimated weights involved in computation [26]. They may require lengthy
training times and the use of random weight initializations may lead to different solutions [37].
Despite successful applications, ANN remain problematic in that they offer us little or no insight
into the process(es) by which they learn or the totality of the knowledge embedded in them [38].
Several limitations of ANN are identified in the literature: they are limited in their ability to explic-
itly identify possible causal relationships, they are challenging to use in the field, they are prone to
over fitting, model development is empirical potentially requiring several attempts to develop an
acceptable model [37], and there are methodological issues related to model development [31]. In
comparing advantages and disadvantages of using ANN to predict medical outcomes, Tu (1996)
suggests that logistic regression models can be disseminated to a wider audience, whereas ANN
models are less transparent and therefore can be more difficult to communicate and use. Even if
published and made available, the connection weight matrices used in ANN for training a data set
may be large and difficult to interpret for others to make use of, whereas logistic regression coeffi-
cients can be published for any end user to be able to calculate [31].
Methods
The Arksey & O’Malley framework (2005) was adopted to identify the (i) research question,
(ii) relevant studies, (iii) select studies, (iv) chart the data and (v), collate, summarize and pres-
ent findings.
Applications of ANN in health care organizational decision-making: A scoping review
PLOS ONE | https://doi.org/10.1371/journal.pone.0212356 February 19, 2019 6 / 22
Search strategy
Due to the cross-disciplinary nature of our query, the search strategy was designed to identify
literature from multiple databases according to the key disciplines of Health Administration
(Medline and Embase), Computer Science (ACM Digital Library and Advanced Technologies
& Aerospace Database), and Business and Management (ABI/Inform Global and JSTOR). The
selection of the three disciplines reflects the core concepts embedded in our research question:
‘what are the different applications of ANN (Computer Science) in health care organizational
decision-making (Health Administration and Business Management)?’
In consultation with a librarian, a comprehensive search syntax was built on the concepts of
‘artificial neural networks’ applied in ‘health care organizational decision-making’ and tailored
for each database for optimum results. The final search syntax was based on search terms
refined through an iterative process involving examination of a preliminary set of results to
ensure relevance (S1 Appendix). The search strategy was limited to peer-reviewed publications
in English without limitation to the year of publication up until the time of our search (January
2018). Our background search did not identify seminal paper(s) published or advancements
related to our research question, thereby justifying the rationale for not limiting the search to a
specicic start date.
Data collection
Screening of articles occurred in two stages. Identified articles were de-duplicated and
imported to EndNote as a reference manager and to Covidence, a web-based platform, for
screening. The screening inclusion and exclusion criteria were built iteratively via consensus
(NS, TR and WB) (Table 1). Titles and abstracts were first screened to include articles with
keywords related to and/or in explicit reference to artificial neural networks. Articles were
excluded if there was no explicit reference to artificial neural networks; the application was not
in the health care domain or context of health care organizational decision-making, or was not
a publication that was peer-reviewed (e.g. grey literature e.g. conference abstracts and papers,
book reviews, newspaper or magazine articles, teaching courses). Table 1 lists the criteria used
to screen, include or exclude articles in the review.
Subsequently, a full-text review of articles that met the initial screening criteria was con-
ducted on basis of relevance and availability of information for data extraction. In addition to
independent review and extraction of articles, two coders (NS and TR) extracted data from a
subset of articles for consensus, minimization of error, and clarity between reviewers regarding
Table 1. Screening inclusion, exclusion criteria.
Inclusion criteria Exclusion criteria
Titles and
abstracts
Explicit reference to keywords: neural network;
artificial neural network; ANNs;
Does not make explicit reference to artificial
neural networks within the context of healthcare
or medicineMust make reference to ANN if any type of
artificial intelligence or machine learning
techniques used, (e.g. Fuzzy logic, Bayesian
statistics and Self-Organizing Maps, back-
propagation; prediction model; unsupervised
learning)
Publication
Type
Peer-reviewed empirical or theoretical work
(e.g. Journal articles, reports)
Not based on empirical or theoretical work (e.g.
book reviews, newspaper article, course
material); conference papers and abstracts
Setting or
Context
Application in domain of Healthcare and/or
Medicine
Application was not directly related to healthcare
organizational decision making (e.g. speech
recognition)
https://doi.org/10.1371/journal.pone.0212356.t001
Applications of ANN in health care organizational decision-making: A scoping review
PLOS ONE | https://doi.org/10.1371/journal.pone.0212356 February 19, 2019 7 / 22
the choice of data selected for extraction. Information related to study characteristics, aim,
methodology (application, taxonomy, accuracy) and context including organizational level of
analysis (micro-, meso- and macro-) was collected and entered into Microsoft Excel for cate-
gorization and descriptive analysis. Applications of ANN to make decisions directly between
providers and patients was categorized as ‘micro’, any decisions made by a larger group and
not directly related to a patient was categorized as ‘meso’, and decisions beyond an organiza-
tional group (i.e. across different institutions, a system or countries) was categorized as
‘macro’ level of decision-making.
Results
Overall, 3,457 articles were imported for screening, out of which (after removal of duplicates)
3,397 were screened for titles and abstracts to give a total of 306 articles used for full-text
review (Fig 2). Articles were excluded from data collection for reasons such as: there being no
Fig 2. Review process overview. �Articles excluded for the following reasons: Not ANN or suitable synonym (n = 93), use of ANN
unrelated to healthcare organizational decision-making (n = 70), based on iterated exclusion criteria (n = 45), not based on
empirical or theoretical research (n = 9), could not access full-text (n = 9).
https://doi.org/10.1371/journal.pone.0212356.g002
Applications of ANN in health care organizational decision-making: A scoping review
PLOS ONE | https://doi.org/10.1371/journal.pone.0212356 February 19, 2019 8 / 22
explicit reference to ANN being used (91 articles), the application of ANN was not in the con-
text of health care organizational decision-making (68 articles), on basis of study exclusion cri-
teria (53 articles) or the articles were irretrievable (8). In total, 80 articles were used for data
collection. Fig 2 illustrates the overall review process including number of articles excluded at
each stage.
Study characteristics
Publication dates ranged from 1997 to 2018 with the number of studies fluctuating each year
(Fig 3A). Studies were published across 24 countries with the majority of first authors from the
United States (26), the United Kingdom and India (7), Taiwan (6) and Italy (5) (Fig 3B). Fig 3A
and 3B illustrate the number of articles published over the years and across varying countries.
Aim and methodology
Main topics or area of interest based on the article’s overall purpose included Organizational
Behaviour (18%), Cardiovascular (14%), Infectious Disease and Telemedicine (7%) (Table 2).
Topics categorized under ‘Organizational Behaviour’ include: behaviour and perspectives, cri-
sis or risk management, clinical and non-clinical decision-making, and resource management
(S2 Appendix). Table 2 lists the main topic areas of articles reviewed.
Applications of ANN were mainly found to be classification (22), prediction (14), and diag-
nosis (10) (Fig 4). Examples of applications include classification of data in medical databases
(i.e. organizing or distinguishing data by relevant categories or concepts) [39], using a hybrid
learning approach for automatic tissue recognition in wound images for accurate wound eval-
uations [40], and comparison of soft-computing techniques for diagnosis of heart conditions
by processing digitally recorded heart sound signals to extract time and frequency features
related to normal and abnormal heart conditions [41]. Applications for prediction included
developing a risk advisor model to predict the chances of diabetes complication according to
changes in risk factors [42], identifying the optimal subset of attributes from a given set of
attributes for diagnosis of heart disease [43], modelling daily patient arrivals in the Emergency
Department [44]. ANN was applied for diagnosis of disease based on age, sex, body mass
index, average blood pressure and blood serum measurements [45], comparing predictive
accuracies of different types of ANN and statistical models for diagnosis of coronary artery dis-
ease [46], diagnosis and risk group assignment for pulmonary tuberculosis among hospitalized
patients [47], and non-invasive diagnosis of early risk in dengue patients [48]. Other examples
include exploring the potential use of mobile phones as a health promotional tool by tracking
daily exercise activities of people and using ANN to estimate a user’s movement[49], or using
ANN to identify factors related to treatment and outcomes potentially impacting patient
length of stay[50]. In addition to S2 Appendix, Fig 4 illustrates the various applications of
ANN identified in the literature review.
With respect to nomenclature or taxonomy, authors mostly reported using artificial neural
networks (36 articles), feed-forward networks (25 articles), a hybrid model (23 articles), recur-
rent feedback networks (6 articles) or other (3 articles) (S2 Appendix). Various types of data
(e.g. patients, cases, images, and signals) and sample sizes were used. Training/testing sets
were in ratios of 50:50, 70:30 or 90:10 and the reported accuracy ranged between 50% and
100%.
Context and key findings
ANN was primarily applied to organizational decision-making at a micro-level (61 articles)
between patients and health care providers in addition to meso-, macro-levels out of which 48
Applications of ANN in health care organizational decision-making: A scoping review
PLOS ONE | https://doi.org/10.1371/journal.pone.0212356 February 19, 2019 9 / 22
articles referenced to micro-level decision-making only; with 29 articles referencing meso-
level applications between patients, health care providers, hospital managers and decision-
makers, out of which 10 referenced meso- only. A small portion (10) of studies applied ANN
at a macro level of decision-making mainly between policy and decision-makers across multi-
ple facilities or health care systems, out of which 2 referenced macro- only. Micro-level appli-
cations of ANN include diagnosis of pulmonary tuberculosis among hospitalized patients by
Fig 3. Article characteristics. (A) Number of articles by publication year. (B) Number of articles by country.
https://doi.org/10.1371/journal.pone.0212356.g003
Applications of ANN in health care organizational decision-making: A scoping review
PLOS ONE | https://doi.org/10.1371/journal.pone.0212356 February 19, 2019 10 / 22
health care providers using models developed for classification and risk group assignment
[47], classify Crohn’s Disease medical images [51], analyse recorded ECG signals to trigger an
alarm for patients and allow collection and transmission of patient information to health care
providers[52]. Meso-level applications include decision-making among managers involving
classification of cost [53], developing a forecasting model to support health care management
decision-making[54], among patients, providers, and hospital managers in order to evaluate
the effect of hospital employee motivation on patient satisfaction [55], and predicting the
adoption of radio frequency identification (RFID) technology adoption in clinical setting [56].
Macro-level applications of ANN include risk-adjustment models for policy-makers of Tai-
wan’s National Health Insurance program [57], a global comparison of the perception of
Table 2. Study areas identified in the review.
Study Area Number of Articles
Organizational Behaviour 18
Other� 15
Cardiovascular 14
Infectious Disease 7
Telemedicine 7
Finance 5
Trauma 5
Medical Imaging 4
Diabetes 4
Surgery 4
Information Systems 4
�Sub-categories of ‘Other’ articles include: elderly studies, renal disease, medical diagnosis, data mining,
pharmacology, fall detection, disorders (epilepsy or autism).
https://doi.org/10.1371/journal.pone.0212356.t002
Fig 4. Types of applications of artificial neural networks identified in the review.
https://doi.org/10.1371/journal.pone.0212356.g004
Applications of ANN in health care organizational decision-making: A scoping review
PLOS ONE | https://doi.org/10.1371/journal.pone.0212356 February 19, 2019 11 / 22
corruption in the health care sector [58], model revenue generation for decision-makers to
determine best indicators of revenue generation in not-for-profit foundations supporting hos-
pitals of varying sizes [59].
Authors reported neural networks reduced computation time in comparison to conven-
tional planning algorithms [60] thereby enabling users to access model output faster in real-
time, outperforming linear regression models in prediction [44, 56, 61–63] and support vector
machines in classification [64, 65]. Limitations centered around the use of small data sets [42,
53, 66–72], limiting data set to continuous variables [69], inability to examine causal relation-
ships [56] or have the network explain weights applied, appropriateness of decision-making
[71, 73, 74], difficulty in implementation or understanding of the output [75]. ANN were cau-
tioned to be used as a proof of concept rather than a successful prediction model [66].
Discussion
This review provides a comprehensive review of the various applications of artificial neural
networks in health care organizational decision-making. To our knowledge, this is the first
attempt to comprehensively describe the use of ANN in health care, from the time of its origins
to current day use, on all levels of organizational decision-making.
Prior efforts have concentrated on a specific domain or aspect of health care and/or limited
study findings to a period of time. A systematic review on the use of ANN as decision-making
tools in the field of cancer reported trends from 1994–2003 in clinical diagnosis, prognosis and
therapeutic guidance for cancer from1994 to 2003, and suggested the need for rigorous meth-
odologies in using neural networks [19]. Another review reported various applications in areas
of accounting and finance, health and medicine, engineering and marketing, however focused
the review on feed-forward neural networks and statistical techniques used in prediction and
classification problems [20]. Outside of medicine and health care, Wong et al. conducted liter-
ature reviews of ANN used in business (from 1988–1995) [76] and finance (1990–1996) [77],
at that time describing the promise of neural networks for increasing integration with other
existing or developing technologies [76, 77]. Data mining is the mathematical core of a larger
process of knowledge discovery from databases otherwise referred to as the ‘KDD process
[78]. The main activities involved in the KDD process include (i) integration and cleaning, (ii)
selection and transformation, (iii) data mining and (iv) evaluation and interpretation. Data
mining pertains to extraction of significant patterns and knowledge discovery and employs
inferring algorithms, such as ANN, to pre-processed data to complete data mining tasks such
as classification and cluster analysis [79]. Data mining and machine learning have produced
practical applications in areas of analysing medical outcomes, detecting credit card fraud, pre-
dicting customer purchase behaviour or predicting personal interests from internet use [80].
Although limited in scope to the field of infertility, Durairaj & Ranjani (2013) conducted a
comparative study of data mining techniques including ANN, suggesting the promise of com-
bining more than one data mining technique for diagnosing or predicting disease [81].
Due to the primitive nature of computer technology mid-20 th
Century, most of the research
in machine learning was theoretical or based on construction of special purpose systems [18].
We found that application of ANN in health care decision-making began in the late 90’s with
fluctuating use over the years. A number of breakthroughs in the field of computer science and
AI bring insight to reported publication patterns [82]. ANN gained prominence with the pub-
lication of a few seminal works including the publication of the backpropagation learning rule
for multilayered feed-forward neural networks [22]. In 1986, backpropagation was proven as a
general purpose and simple procedure, powerful enough for a multi-layered neural network to
use and construct appropriate internal representations based on incoming data [83]. A few
Applications of ANN in health care organizational decision-making: A scoping review
PLOS ONE | https://doi.org/10.1371/journal.pone.0212356 February 19, 2019 12 / 22
years later, the ability of neural networks to learn any type of function was demonstrated [84],
suggesting capabilities of neural networks as universal approximators [85]. During the 90’s,
most of the research was largely experimental and the need for use of ANN as a widely-used
computer paradigm remained warranted [18].
With the digitization of health care [86], hospitals are increasingly able to collect large
amounts of data managed across large information systems [22]. With its ability to process
large datasets, machine learning technology is well-suited for analysing medical data and pro-
viding effective algorithms [22]. Considering the prevalent use of medical information systems
and medical databases, ANN have found useful applications in biomedical areas in diagnosis
and disease monitoring [87].
Although the backpropagation learning rule enabled the use of neural networks in many
hard medical diagnostic tasks, they have been typically used as black box classifiers lacking the
transparency of generating knowledge as well as the ability to explain decision-making [22].
The lack of transparency or interpretability of neural networks continues to be an important
problem since health care providers are often unwilling to accept machine recommendations
without clarity regarding the underlying rationale [88]. Prior to 2006, application of neural
networks included processing of biomedical signals, for example image and speech processing
[89, 90], clinical diagnosis, image analysis and interpretation, and drug development [87]. In
2006, a critical paper described the ability of a neural network to learn faster [91]. Six years
later, the largest deep neural network to date (i.e. depth pertaining to layers of the network),
was trained to classify 1.2 million images in record-breaking time as part of the ImageNet
Large Scale Visual Recognition Challenge [92].
The most successful applications of ANN are found in extremely complex medical situa-
tions [13]. We found ANN to be mainly used for classification, prediction and clinical diagno-
sis in areas of cardiovascular, telemedicine and organizational behaviour. Use of ANN applies
to four general areas of cardiovascular medicine: diagnosis and treatment of coronary artery
disease, general interpretation of electrocardiography, cardiac image analysis and cardiovascu-
lar drug dosing [93]. Telemedicine offers health care providers elaborate solutions for remote
monitoring designed to prevent, diagnose, manage disease and treatment [94] and can include
machine learning techniques to predict clinical parameters such as blood pressure [95]. Pre-
liminary diagnosis of high-risk patients (for disease or attributes) using neural networks pro-
vide hospital administrators with a cost-effective tool in time and resource management [16].
Neural networks have been used effectively as a tool in complex decision-making in strate-
gic management, specifically in strategic planning and performance, assessing decision-mak-
ing [96]. In health care, neural network models have been successfully used to predict quality
determinants (responsiveness, security, efficiency) influencing adoption of e-government ser-
vices [97]. With its ability to discover hidden knowledge and values, scholars have suggested
using ANN to improve care performance and facilitate the adoption of ‘Lean thinking’ or
value-based decision making in health care [87]. An example of ANN facilitating Lean think-
ing adoption in health care contexts is its application to describe ‘information flow’ among
cancer patients by modeling the relationship between quality of life evaluations made by
patients, pharmacists and nurses [87]. ‘Flow’ is a key concept in a Lean System and ‘informa-
tion flow’ is an essential improvement target to the successful operation of a health care system
using a Lean approach [87]. Key success factors or differentiators that define effective machine
learning technology in health care include access to extensive data sources, ease of implemen-
tation, interpretability and buy-in as well as conformance with privacy standards [9]. Support
vector machines are used to model high-dimensional data and are considered state-of-the-art
solutions to problems otherwise not amenable to traditional statistical analysis. Despite its ana-
lytic capabilities, wide-scale adoption remains a challenge, mainly due to methodological
Applications of ANN in health care organizational decision-making: A scoping review
PLOS ONE | https://doi.org/10.1371/journal.pone.0212356 February 19, 2019 13 / 22
complexities and scalability challenges [98]. For example, a systematic review of deep learning
models using electronic health record data recently identified challenges related to the tempo-
rality (e.g. hidden relationships among clinical variables occurring at short and long term
events) and irregularity of information used which can reduce model performance if not han-
dled appropriately [88]. Poor interpretability remains a signicant challenge with implementing
ANN in health care [90]. Zhang et al (2018) report that in comparison to linear models, ANN
are not only difficult to interpret but the identification of predictors (input features) important
for the model also seem to be a challenge [99]. Fisher et al (2016) developed an ANN based
monitoring method evaluating Parkinson’s disease motor symptoms and reported signiciant
challenges with detecting disease states due to the inherent subjectivity underlying the inter-
pretation of disease state descriptors (i.e. the degree of motor symptoms experienced by each
patient would likely vary) [100]. Despite the evident progress in certain areas (e.g. knowledge
and temporal representation, machine learning), the adoption of key standards required for
integration and knowledge sharing (e.g. controlled terminologies, semantic structuring, stan-
dards representing clinical decision logic) has been slow [101] Patel et al. (2009) suggest barri-
ers to progress are related to political, fiscal or cultural reasons and not purely technical. A
national study on the implementation of Health Information Technology (HIT) in the United
States reported a poor understanding of IT staff, informaticians, health information managers
and others playing a significant role in implementation of HIT in health care [102] Barriers to
adoption of HIT include mismatch of return on investment, challenges to workflow in clinical
settings, lack of standards and interoperability, and concerns about privacy and confidentiality
[102].
We found that researchers often adopted a hybrid approach when using neural networks.
Hybrid approaches (e.g. combining two or more techniques/soft-computing paradigms) are
effective in reducing challenges with neural networks when introducing new items to the sys-
tem or having insufficient data [103]. ANN learn (supervised, unsupervised or reinforcement)
based on the iterative adjustment of connection weights using optimization algorithms such as
the backpropagation rule. Challenges related to such algorithms include the necessity of a pre-
viously defined architecture for the model, sensitivity to the initial conditions used in training
[104]. A hybrid model of an ANN and decision tree classifier has been used to predict univer-
sity admissions using data related to student academic merits, background and university
admission criteria. Reported advantages of using a hybrid model included higher prediction
accuracy rates (error rate of <2%), flexibility and faster performance (0.1 second) in compari-
son with a model using neural networks only (20 minutes learning time). Another advantage
reported was improved generalizability, e.g. ability to understand rules extracted that can be
later coded into another type of system [105] Literature suggests extensive use of ANN in busi-
ness applications in particular areas related to financial distress and bankruptcy problems,
stock price forecasting and decision support [106] Hybrid networks have also been developed
in business applications to improve performance of standard models [106]. The integration of
ANN with secondary AI and meta-heuristic methods such as fuzzy logic, genetic, bee colony
algorithms, or artificial immune systems have been proposed to reduce or eliminate challenges
related to ANN (e.g. selection of network topology, initial weights, choice of control parame-
ters) [106]. Applications of hybrid intelligent systems include robotics, medical diagnosis,
speech/natural language understanding, monitoring of manufacturing processes.
Our findings suggest a possible correlation between advancements made in the field of
ANN and publication rates related to the application of ANN in health care organizational
decision-making. Despite the variety of study contexts and applications, ANN continues to be
mainly used for classification, prediction and diagnosis. As suggested by the literature, the
most commonly used taxonomy of ANN found was the feed-forward neural network.
Applications of ANN in health care organizational decision-making: A scoping review
PLOS ONE | https://doi.org/10.1371/journal.pone.0212356 February 19, 2019 14 / 22
However, our study showed a significant use of hybrid models. ANN’s application to facilitate
more micro- and meso-level decision-making compared to macro-level may be explained by
the type and volume of data required and available to build an effective model.
Strengths and limitations
A primary strength of this review is its comprehensive scope and search strategy involving
multiple databases. Variables selected for data collection were based on bodies of work with
similar inquiry and well aligned with the methods of a scoping review. The complex nature of
artificial neural networks required a fundamental understanding for the authors who were
otherwise novice to the field. Studies included in this review did not always use standardized
reporting measures and may include publications of lower quality.
Implications
Practical implications
Current and anticipated advancements in the field of AI will play an influential role in decision-
making related to adopting novel and innovative machine learning based techniques in health
care. Clinical applications of AI include analysis of electronic health records, medical image pro-
cessing, physician and hospital error reduction [107] AI applications in workflow optimization
include payer claim processing, network coordination, staff management, training and educa-
tion, supply costs and management [107] For example, the top three applications of greatest
near-term value (based on the impact of application, likelihood of adoption and value to health
economy) are reported to be robot-assisted surgery (valued at $40 B), virtual nursing assistants
($20B) and administrative workflow assistance ($18 B) [108]. Applications with lowest esti-
mated potential value include preliminary diagnosis ($5B), automated image ($3B) and cyber-
security ($2B) [108]. Our findings warrant the understanding of perspectives and beliefs of
those adopting ANN-based solutions in clinical and non-clinical decision-making.
Patients and families are accessing health information in real-time with the array of AI or
ANN based health care solutions available to them in an open and unstructured market. Clini-
cal applications of ANN-based solutions can have implications on the changing role of health
care providers as well team dynamics and patterns in workflow. The changing role of the phy-
sicians has been at the forefront of recent debates on AI, with some anticipating the positive
impacts of augmenting clinical service with AI based technologies, e.g., enabling early diagno-
sis, or improving understanding of a patient’s medical history with genetic sequencing [109].
Literature suggests a need for bridging disciplines in order to enable of clinicians to benefit
from rapid advancements in technology [101] In addition to the implications for clinical deci-
sion-making, interprofessional team dynamics and processes can be expected to change. For
example, a US based hospital has collaborated with a game development company to create a
virtual world in which surgeons are guided through scenarios in the operating room using
rules, conditions and scripts to practice making decisions, team communication, and leader-
ship [110].
As policy-makers adopt strategies towards a value-based, patient-centred model of care
delivery, decision-makers are required to consider the readiness of health care organizations
for successful implementation and wide-scale adoption of AI or ANN based decision-support
tools. Factors such as easier integration with hospital workflows, patient-centric treatment
plans leading to improved patient outcomes, elimination of unnecessary hospital procedures
and reduced treatment costs can influence wider adoption of AI-based solutions in the health
care industry [107]. Challenges in uptake include the current inability of AI-based solutions to
read unstructured data, the perspectives of health care providers using AI-based solutions, and
Applications of ANN in health care organizational decision-making: A scoping review
PLOS ONE | https://doi.org/10.1371/journal.pone.0212356 February 19, 2019 15 / 22
the lack of supportive infrastructure required for wide-scale implementation [107]. For
improved organizational readiness, the governance and operating model of health care organi-
zations need to enable a workforce and culture that will support the use of AI to enhance effi-
ciency, quality and patient outcomes [108].
Machine learning from unstructured data (e.g. patient health records, photos, reviews,
social media data from mobile applications and devices) remain a critical unmet need for hos-
pitals [107, 111]. Currently, most of the data in health care is unstructured and difficult to
share [107] Wide-scale implementation and adoption of AI service solutions requires strong
partnerships between AI technology vendors and health care organizations [107]. Policies
encouraging transparency and sharing of core datasets across public and private sectors can
stimulate higher levels of innovation-oriented competition and research productivity [112].
Theoretical implications
Several theoretical implications emerge from our study findings. Healthcare organizations are
complex adaptive systems embedded in larger complex adaptive systems[113]; health care
organizational decision-making can appropriately rely on ANN as an internalized rule set. The
change of health care delivery from single to multiple settings and providers has led to new
complexities around how health care delivery needs are being structured and managed (e.g.,
support required for delivering collaborative care or patient participatory medicine) [1]. Tradi-
tional decision-making processes based on stable and predictable systems are no longer rele-
vant, due to the complex and emergent nature of contemporary health care delivery systems
[1]. Yet the health care organizational decision-making literature suggests the focus of deci-
sion-making persistently remains on problems that are visible, while the larger system within
which health care delivery organizations exist remains unacknowledged [1]. Using complex
adaptive systems (CAS) theory to understand the functionality of AI can provide critical
insights: first, AI enhances adaptability to change by strengthening communication among
agents, which in turn fosters rapid collective response to change, and further, AI possesses the
potential to generate a collective memory for social systems within an organization [114].
The theory of CAS has been used as an alternative approach to improve our understanding
and scaling up of health services; CAS theory shifts decision-making towards embracing
uncertainty, non-linear processes, varying context and emergent characteristics [115]. Interde-
pendent organizational factors such as clinical practice, organization, information manage-
ment research education and professional development, are built around multiple self-
adjusting interacting systems [116]. Agents (e.g. users of the system) respond to their environ-
ment based on internalized rule sets that are not necessarily explicit, shared or need to be
understood by another agent [116]. Although lacking the ability to explain decision-making,
ANN-based decision-support tools enable health care organizational decision-makers to
respond to complex and emergent environments using incoming and evolving data.
Conclusion
Our study found artificial neural networks can be applied across all levels of health care organi-
zational decision-making. Influenced by advancements in the field, decision-makers are taking
advantage of hybrid models of neural networks in efforts to tailor solutions to a given problem.
We found ANN-based solutions applied on the meso- and macro-level of decision-making
suggesting the promise of its use in contexts involving complex, unstructured or limited infor-
mation. Successful implementation and adoption may require an improved understanding of
the ethical, societal, and economic implications of applying ANN in health care organizational
decision-making.
Applications of ANN in health care organizational decision-making: A scoping review
PLOS ONE | https://doi.org/10.1371/journal.pone.0212356 February 19, 2019 16 / 22
Supporting information
S1 Checklist. Preferred Reporting Items for Systematic Reviews and Meta-Analyses
(PRISMA) checklist.
(PDF)
S1 Appendix. Search strategy and syntax.
(PDF)
S2 Appendix. Summary of findings.
(PDF)
S3 Appendix. Glossary of terms.
(PDF)
S1 Workflow. Preferred Reporting Items for Systematic Reviews and Meta-Analyses
(PRISMA) flowchart.
(PDF)
Author Contributions
Conceptualization: Nida Shahid, Whitney Berta.
Data curation: Nida Shahid.
Formal analysis: Nida Shahid, Tim Rappon, Whitney Berta.
Investigation: Nida Shahid, Tim Rappon, Whitney Berta.
Methodology: Nida Shahid, Tim Rappon, Whitney Berta.
Project administration: Nida Shahid.
Supervision: Whitney Berta.
Writing – original draft: Nida Shahid.
Writing – review & editing: Nida Shahid, Tim Rappon, Whitney Berta.
References 1. Kuziemsky C. Decision-making in healthcare as a complex adaptive system. Healthc Manage Forum.
2016; 29(1):4–7. https://doi.org/10.1177/0840470415614842 PMID: 26656389.
2. Deloitte. Global health care outlook: The evolution of smart health care. 2018.
3. Young DW, Ballarin E. Strategic decision-making in healthcare organizations: it is time to get serious.
Int J Health Plann Manage. 2006; 21(3):173–91. PMID: 17044545.
4. Singh JV. Slack, and Risk Taking in Organizational Decision-Making The Academy of Management
Journal. 1986; 29(3):562–85.
5. French R, Rayner C, G R, Rumbles S, Schermerhorn J, Hunt J, et al. Organizational Behaviour. 2nd
ed. New York: John Wiley & Sons; 2011.
6. Awowale A. Decision Making in Healthcare Systems: Roles and Responsibilities: University of Mary-
land; 2017.
7. IBM Corporation. Data-driven healthcare organizations use big data analytics for big gains. Somers,
NY: IBM Corporation, 2013.
8. Grand View Research. Healthcare Predictive Analytics Market Analysis By Application (Operations
Management, Financial, Population Health, Clinical), By End-Use (Payers, Providers), By Region
(North America, Europe, Asia Pacific, Latin America, MEA) And Segment Forecasts, 2018–2025.
2016 November 2016. Report No.
Applications of ANN in health care organizational decision-making: A scoping review
PLOS ONE | https://doi.org/10.1371/journal.pone.0212356 February 19, 2019 17 / 22
9. Tatcher L. The Dangers of Commoditized Machine Learning in Healthcare: 5 Key Differentiators that
Lead to Success: Healthcare.ai; 2018 [cited 2018]. Available from: https://healthcare.ai/dangers-of-
commoditized-machine-learning-in-healthcare/.
10. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and
future. Stroke Vasc Neurol. 2017; 2(4):230–43. https://doi.org/10.1136/svn-2017-000101 PMID:
29507784; PubMed Central PMCID: PMCPMC5829945.
11. Lee CW, Park JA. Assessment of HIV/AIDS-related health performance using an artificial neural net-
work. Information & Management. 2001; 38(4):231–8. https://doi.org/10.1016/S0378-7206(00)00068-
9 PubMed PMID: WOS:000165979600003.
12. Sordo M. Introduction to Neural Networks in Healthcare. 2002.
13. Bartosch-Harlid A, Andersson B, Aho U, Nilsson J, Andersson R. Artificial neural networks in pancre-
atic disease. Br J Surg. 2008; 95(7):817–26. https://doi.org/10.1002/bjs.6239 PMID: 18551536.
14. Goss EP, Vozikis GS. Improving Health Care Organizational Management Through Neural Network
Learning. Health Care Management Science. 2002; 5(3):221–7. PMID: 12363049
15. Kaur H, Wasan SK. Empirical Study on Applications of Data Mining Techniques in Healthcare. Journal
of Computer Science. 2006; 2(2):194–200.
16. Nolting J. Developing a neural network model for health care. AMIA Annu Symp Proc. 2006: 1049.
PMID: 17238669; PubMed Central PMCID: PMCPMC1839654.
17. Somers J. Is AI Riding the One-Trick Pony? MIT Technology Review. 2017.
18. Schocken S, Ariav G. Neural Networks for Decision Support Systems: Problems and Opportunities.
New York: Center for Research on Information Systems, Stern School of Business, New York Univer-
sity, 1991.
19. Lisboa PJ, Taktak AF. The use of artificial neural networks in decision support in cancer: a systematic
review. Neural Netw. 2006; 19(4):408–15. https://doi.org/10.1016/j.neunet.2005.10.007 PMID: 16483741.
20. Paliwal M, Kumar U. Neural networks and statistical techniques: A review of applications. Expert Sys-
tems with Applications. 2009; 36(1):2–17.
21. Guler S, Hurton S, C winn M, Molinari M. Levels in Decision Making and Techniques for Clinicians.
International Journal of Digestive Diseases. 2015; 1(1:2).
22. Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif
Intell Med. 2001; 23(1):89–109. PMID: 11470218.
23. Scarborough D, Somers MJ. Neural Networks in Organizational Research: Applying Pattern Recogni-
tion to the Analysis of Organizational Behaviour. Washington, D.C.: American Psychological Associa-
tion; 2006.
24. Sarle W, editor Neural Networks and Statistical Models. Nineteenth Annual SAS Users Group INterna-
tional Conference; 1994; Cary, NC, USA.
25. Kayri M. Data Optimization with Multilayer Perceptron Neural Network and Using New Pattern in Deci-
sion Tree Comparitively. Journal of Computer Science. 2010; 6(5):606–12.
26. Agatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its
application in pharmaceutical research. J Pharm Biomed Anal. 2000; 22(5):717–27. PMID: 10815714.
27. da Silva IN, Hernane Spatti S, Andrade Flauzino R. Chapter 2: Artificial Neural Network Architectures
and Training Processes. Artificial Neural Networks: A Practical Course. Switzerland: Springer Inter-
national Publishing 2017.
28. Penny W, Frost D. Neural Networks in Clinical Medicine. Medical Decision Making. 1996; 16:386–98.
https://doi.org/10.1177/0272989X9601600409 PMID: 8912300
29. Forrstrom J, Dalton KJ. Artificial Neural Networks for Decision Support in Clinical Medicine. Annals of
Medicine. 1995; 27(5):509–17. PMID: 8541025
30. Sharma A, Chopra A. Artificial Neural Networks: Applications in Management. Journal of Business
and Management. 2013; 12(5):32–40.
31. Tu JV. Advantages and disadvantages of using artificial neural networks versus logistic regression for
predicting medical outcomes. J Clin Epidemiol. 1996; 49(11):1225–31. PMID: 8892489.
32. Han J, Pei J, Kamber M. Data Mining: Concepts and Techniques. Waltham, MA: Elsevier Inc.; 2012.
33. Picard RR, Ber KN. Data Splitting. The American Statistician 2010; 44(2):140–7.
34. Reitermanova Z. Data Splitting. WDS’10 Proceedings of Contributed Papers. 2010;Part 1:31–6. 978-
80-7378-139-2.
35. May RJ, Maier HR, Dandy GC. Data splitting for artificial neural networks using SOM-based stratified
sampling. Neural Networks. 2010; 23:283–94. https://doi.org/10.1016/j.neunet.2009.11.009 PMID:
19959327
Applications of ANN in health care organizational decision-making: A scoping review
PLOS ONE | https://doi.org/10.1371/journal.pone.0212356 February 19, 2019 18 / 22
36. Amazon. Amazon Machine Learning: Developer Guide. 2018.
37. McGarry K, Wermster S, MacIntyre J. Hybrid Neural Systems: From SImple Coupling to Fully Inte-
grated Nueral Networks. Neural Computing Surveys 2. 1999:62–93.
38. Tickle AB, Andrews R, Golea M, Diederich J. The truth will come to light: directions and challenges in
extracting the knowledge embedded within trained artificial neural networks. IEEE Trans Neural Netw.
1998; 9(6):1057–68. https://doi.org/10.1109/72.728352 PMID: 18255792.
39. Verma B, Hassan SZ. Hybrid ensemble approach for classification. Applied Intelligence. 2011; 34
(2):258–78. http://dx.doi.org/10.1007/s10489-009-0194-7. PMID: 856176289.
40. Veredas FJ, Mesa H, Morente L. A hybrid learning approach to tissue recognition in wound images.
International Journal of Intelligent Computing and Cybernetics. 2009; 2(2):327–47. http://dx.doi.org/
10.1108/17563780910959929. PubMed PMID: 228078109.
41. Samanta B, Nataraj C. Automated diagnosis of cardiac state in healthcare systems using computa-
tional intelligence. International Journal of Services Operations and Informatics. 2008; 3(2):162–77.
http://dx.doi.org/10.1504/IJSOI.2008.019331. PMID: 34063662; 200809-B7-0052794 (MB); 200809-
51-0177345 (CI).
42. Sangi M, Win KT, Shirvani F, Mohammad-Reza N- R, Shukla N. Applying a Novel Combination of
Techniques to Develop a Predictive Model for Diabetes Complications. PLoS One. 2015; 10(4). http://
dx.doi.org/10.1371/journal.pone.0121569. PubMed PMID: 1691593088.
43. Vivekanandan T, Iyengar NCSN. Optimal feature selection using a modified differential evolution algo-
rithm and its effectiveness for prediction of heart disease. Computers in Biology and Medicine. 2017;
90:125–36. http://dx.doi.org/10.1016/j.compbiomed.2017.09.011. PubMed PMID: 1954368817.
https://doi.org/10.1016/j.compbiomed.2017.09.011 PMID: 28987988
44. Xu M, Wong TC, Chin KS. Modeling daily patient arrivals at Emergency Department and quantifying
the relative importance of contributing variables using artificial neural network. Decision Support Sys-
tems. 2013; 54(3):1488. PubMed PMID: 1313261404.
45. Olatubosun O, Olusoga F, Abayomi F. Diabetes Diagnosis with Maximum Covariance Weighted Resil-
ience Back Propagation Procedure. International Journal of Health and Economic Development.
2015; 1(2):1–13. PubMed PMID: 1690740202.
46. Süt N, Senocak M. Assessment of the performances of multilayer perceptron neural networks in com-
parison with recurrent neural networks and two statistical methods for diagnosing coronary artery dis-
ease. Expert Systems. 2007; 24(3):131. http://dx.doi.org/10.1111/j.1468-0394.2007.00425.x. PMID:
196936703.
47. Aguiar FS, Torres RC, Pinto JV, Kritski AL, Seixas JM, Mello FC. Development of two artificial neural
network models to support the diagnosis of pulmonary tuberculosis in hospitalized patients in Rio de
Janeiro, Brazil. Medical and Biological Engineering and Computing. 2016; 54(11):1751–9. http://dx.
doi.org/10.1007/s11517-016-1465-1. PMID: 27016365.
48. Ibrahim F, Faisal T, Mohamad Salim MI, Taib MN. Non-invasive diagnosis of risk in dengue patients
using bioelectrical impedance analysis and artificial neural network. Medical and Biological Engineer-
ing and Computing. 2010; 48(11):1141–8. http://dx.doi.org/10.1007/s11517-010-0669-z. PubMed
PMID: 776582910; https://doi.org/10.1007/s11517-010-0669-z PMID: 20683676.
49. Anderson I, Maitland J, Sherwood S, Barkhuss L, Chalmers M, Hall M, et al. Shakra: Tracking and
Sharing Daily Activity Levels with Unaugmented Mobile Phones. Mobile Networks and Applications.
2007; 12(2–3):185–99.
50. Kudyba S, Gregorio T. Identifying factors that impact patient length of stay metrics for healthcare pro-
viders with advanced analytics. Health Informatics J. 2010; 16(4):235–45. https://doi.org/10.1177/
1460458210380529 PMID: 21216804.
51. Ahmed SS, Dey N, Ashour AS, Sifaki-pistolla D, Balas-timar D, Balas VE, et al. Effect of fuzzy parti-
tioning in Crohn’s disease classification: a neuro-fuzzy-based approach. Medical and Biological Engi-
neering and Computing. 2017; 55(1):101–15. http://dx.doi.org/10.1007/s11517-016-1508-7. PubMed
PMID: 1856582777. https://doi.org/10.1007/s11517-016-1508-7 PMID: 27106754
52. Atoui H, Telisson D, Fayn J, Rubel P. Ambient Intelligence and Pervasive Architecture Designed within
the EPI-MEDICS Personal ECG Monitor. International Journal of Healthcare Information Systems and
Informatics. 2008; 3(4):69–80. PMID: 33773373; 200809-B7-0052020 (MB); 200809-82-0176356
(CI).
53. Berardi VL, Zhang GP. The effect of misclassification costs on neural network classifiers. Decision Sci-
ences. 1999; 30(3):659–82. PMID: 198182834.
54. Eswaran C, Logeswaran R. A dual hybrid forecasting model for support of decision making in health-
care management. Advances in Engineering Software. 2012; 53:23–32. http://dx.doi.org/10.1016/j.
advengsoft.2012.07.006. PubMed PMID: 1770299318; PQ0002257828.
Applications of ANN in health care organizational decision-making: A scoping review
PLOS ONE | https://doi.org/10.1371/journal.pone.0212356 February 19, 2019 19 / 22
55. Hanafizadeh P, Paydar NR, Aliabadi N. Neural Network-based Evaluation of the Effect of the Motiva-
tion of Hospital Employees on Patients’ Satisfaction. International Journal of Healthcare Information
Systems and Informatics. 2010; 5(4):1–19. http://dx.doi.org/10.4018/jhisi.2010100101. PubMed
PMID: 1671366574; 16764023.
56. Chong AY- L, Liu MJ, Luo J, Keng-Boon O. Predicting RFID adoption in healthcare supply chain from
the perspectives of users. International Journal of Production Economics. 2015; 159:66–75. http://dx.
doi.org/10.1016/j.ijpe.2014.09.034. PubMed PMID: 1669878031; PQ0001074931.
57. Hsu S, Lin C, Yang Y. INTEGRATING NEURAL NETWORKS FOR RISK-ADJUSTMENT MODELS.
Journal of Risk and Insurance. 2008; 75(3):617–42. PMID: 226937475.
58. Buscema PM, Gitto L, Russo S, Marcellusi A, Fiori F, Maurelli G, et al. The perception of corruption in
health: AutoCM methods for an international comparison. Quality and Quantity. 2017; 51(1):459–77.
http://dx.doi.org/10.1007/s11135-016-0315-4. PubMed PMID: 1857580006.
59. Malliaris ME, Pappas M. Revenue Generation In Hospital Foundations: Neural Network Versus
Regression Model Recommendations. International Journal of Management and Information Sys-
tems. 2011; 15(1):59–66. PubMed PMID: 853756956.
60. Bajo J, de Paz JF, de Paz Y, Corchado JM. Integrating case-based planning and RPTW neural net-
works to construct an intelligent environment for health care. Expert Systems with Applications. 2009;
36(3):5844–58. http://dx.doi.org/10.1016/j.eswa.2008.07.029. PMID: 33360064; 200904-65-0021007
(CI).
61. Bourdès V, Ferrières J, Amar J, Amelineau E, Bonnevay S, Berlion M, et al. Prediction of persistence of combined evidence-based cardiovascular medications in patients with acute coronary syndrome
after hospital discharge using neural networks. Medical and Biological Engineering and Computing.
2011; 49(8):947–55. http://dx.doi.org/10.1007/s11517-011-0785-4. PubMed PMID: 878056273;
https://doi.org/10.1007/s11517-011-0785-4 PMID: 21598000.
62. Chesney T, Penny K, Oakley P, Davies S, Chesney D, Maffulli N, et al. Data Mining Medical Informa-
tion: Should Artificial Neural Networks Be Used to Analyse Trauma Audit Data? International Journal
of Healthcare Information Systems and Informatics. 2006; 1(2):51. PMID: 35350817; 200902-B7-
0003850 (MB); 200902-82-0010190 (CI).
63. Ernest Preston G, Vozikis GS. Improving Health Care Organizational Management Through Neural
Network Learning. Health Care Management Science. 2002; 5(3):221–7. PMID: 12363049;
12363049.
64. Chao C-M, Yu C-T, Cheng B-W. DATA MINING MODELS FOR DIAGNOSING ACUTE RENAL FAIL-
URE. International Journal of Organizational Innovation (Online). 2012; 4(4):155–74. PubMed PMID:
1011813323.
65. Oliva JT, Garcia Rosa JL. How an epileptic EEG segment, used as reference, can influence a cross-
correlation classifier? Applied Intelligence. 2017; 47(1):178–96. http://dx.doi.org/10.1007/s10489-016-
0891-y. PubMed PMID: 1906055411.
66. Belmonte M. Prediction of attention in autism from single-trial EEG using artificial neural networks.
SIGBIO Newsl. 1997; 17(2):2–15. https://doi.org/10.1145/270572.270573
67. Emanet N, Öz HR, Bayram N, Delen D. A comparative analysis of machine learning methods for clas-
sification type decision problems in healthcare. Decision Analytics. 2014; 1(1):1–20. http://dx.doi.org/
10.1186/2193-8636-1-6. PubMed PMID: 1652776285.
68. Grossi E, Compare A, Buscema M. The concept of individual semantic maps in clinical psychology: a
feasibility study on a new paradigm. Quality and Quantity. 2014; 48(1):15–35. http://dx.doi.org/10.
1007/s11135-012-9746-8. PubMed PMID: 1473700449.
69. Lin C-f, Leboulluec AK, Zeng L, Chen VC, Gatchel RJ. A decision-making framework for adaptive pain
management. Health Care Management Science. 2014; 17(3):270–83. http://dx.doi.org/10.1007/
s10729-013-9252-0. PubMed PMID: 1555370084; https://doi.org/10.1007/s10729-013-9252-0 PMID:
23974825.
70. Liu NT, Holcomb JB, Wade CE, Batchinsky AI, Cancio LC, Darrah MI, et al. Development and valida-
tion of a machine learning algorithm and hybrid system to predict the need for life-saving interventions
in trauma patients. Medical and Biological Engineering and Computing. 2014; 52(2):193–203. http://
dx.doi.org/10.1007/s11517-013-1130-x. PubMed PMID: 1491042940; https://doi.org/10.1007/
s11517-013-1130-x PMID: 24263362.
71. Oztekin A. An Analytical Approach to Predict the Performance of Thoracic Transplantations. Journal
of Centrum Cathedra. 2012; 5(2):185–206. PubMed PMID: 1082041429.
72. Zhang S, Tjortjis C, Zeng X, Qiao H, Buchan I, Keane J. Comparing data mining methods with logistic
regression in childhood obesity prediction. Information Systems Frontiers. 2009; 11(4):449–60. http://
dx.doi.org/10.1007/s10796-009-9157-0. PubMed PMID: 232058422.
Applications of ANN in health care organizational decision-making: A scoping review
PLOS ONE | https://doi.org/10.1371/journal.pone.0212356 February 19, 2019 20 / 22
73. Goodey RD, Brickley MR, Hill CM, Shepherd JP. A controlled trial of three referral methods for patients
with third molars. British Dental Journal. 2000; 189(10):556–60. PMID: 11128259
74. Sheikhtaheri A, Sadoughi F, Hashemi Dehaghi Z. Developing and Using Expert Systems and Neural
Networks in Medicine: A Review on Benefits and Challenges. Journal of Medical Systems. 2014; 38
(9):1–110. http://dx.doi.org/10.1007/s10916-014-0110-5. PubMed PMID: 1551595080; https://doi.org/
10.1007/s10916-014-0110-5 PMID: 25027017.
75. Houston AL, Chen H, Hubbard SM, Schatz BR, Ng TD, Sewell RR, et al. Medical Data Mining on the
Internet: Research on a Cancer Information System. The Artificial Intelligence Review. 1999; 13(5–
6):437–66. http://dx.doi.org/10.1023/A:1006548623067. PMID: 198128879.
76. Wong BK, Bondnovich T, Selvi Y. Neural network applications in business: A review and analysis of
the literature (1988–1975). Decision Support Systems. 1997; 19(4):301–20.
77. Wong BK, Selvib Y. Neural network applications in finance: A review and analysis of literature (1990–
1996). Information & Management. 1998; 34(3):129-.
78. Maimon OZ, Rokach L. Data Mining and Knowledge Discovery Handbook. Second ed. New York:
Springer; 2010 2010.
79. Stahl F, Jordanov I. An overview of the use of neural networks for data mining tasks. WIREs Data Min-
ing Knowledge Discovery. 2012; 2:193–208. https://doi.org/10.1002/widm.1052
80. Mitchell TM. Machine Learning and Data Mining. Communications of the ACM. 1999; 42(11).
81. Durairaj M, Ranjani V. Data Mining Applications In Healthcare Sector: A Study. International Journal of
Scientific & Technology Research. 2013; 2(10).
82. McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathe-
matical Biology. 1943; 52(1/2):99–115; discussion 73–97. PMID: 2185863.
83. Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature.
1986;323.
84. Cybenko G. Approximation by Superpositions of a Sigmoidal Function. Mathematics of Control, Sig-
nals and Systems. 1989; 2:303–14.
85. Hornik K. Approximation Capabilities of Multilayer Feedforward Networks. Neural Networks 1990;
4:251–7.
86. Anderson CL, Agarwal R. The Digitization of Healthcare: Boundary Risks, Emotion, and Consumer
Willingness to Disclose Personal Health Information. Information Systems Research. 2011; 22
(3):469–90.
87. Moghimi FH, Wickramasinghe N. Chapter 2: Artificial Neural Network for Excellence to Facilitate Lean
Thinking Adoption in Healthcare Contexts. Lean Thinking for Healthcare. New York: Springer Sci-
ence; 2014.
88. Xiao C, Choi E, Sun J. Opportunities and challenges in developing deep learning models using elec-
tronic health records data: a systematic review. J Am Med Inform Assoc. 2018; 25(10):1419–28.
https://doi.org/10.1093/jamia/ocy068 PMID: 29893864; PubMed Central PMCID: PMCPMC6188527.
89. Begg R, Kamruzzaman J, Sarker R. Neural Networks in Healthcare: Potential and Challenges. Her-
shey, United States: Idea Group Publishing; 2006.
90. Ferguson J. Neural Networks in Healthcare 2018 [cited 2018 26 March 2018]. Available from: https://
royaljay.com/healthcare/neural-networks-in-healthcare/.
91. Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput. 2006;
18(7):1527–54. https://doi.org/10.1162/neco.2006.18.7.1527 PMID: 16764513.
92. Krizhevsky A, Sutskever I, Hinton GE. ImageNet Classification with Deep Convolutional Neural Net-
works. 2012.
93. Itchhaporia D, Snow PB, Almassy RJ, Oetgen WJ. Artificial neural networks: current status in cardio-
vascular medicine. J Am Coll Cardiol. 1996; 28(2):515–21. https://doi.org/10.1016/0735-1097(96)
00174-X PMID: 8800133.
94. Lymberis A, editor Smart Wearables for Remote Health Monitoring, from Prevention to Rehabilitation:
Current R&D, Future Challenges. 4th Annual IEEE Conference on Information Technology Applica-
tions in Biomedicine; 2003; United Kingdom.
95. Kwong EW, Wu H, Pang GK. A prediction model of blood pressure for telemedicine. Health Informatics
J. 2016. https://doi.org/10.1177/1460458216663025 PMID: 27496863.
96. Sharma AS, Chopra A. Artificial Neural Networks: Applications In Management. Journal of Business
and Management. 2013; 12(5):32–40.
97. Sharma SK, Govindaluri SM, Gattoufi S. Understanding and predicting the quality determinants of e-
government services: A two-staged regression-neural network model. Journal of Modelling in Manage-
ment. 2015; 10(3):325–40.
Applications of ANN in health care organizational decision-making: A scoping review
PLOS ONE | https://doi.org/10.1371/journal.pone.0212356 February 19, 2019 21 / 22
98. Milenova B, Yarmus JS, Campos MM, editors. SVM in Oracle Database 10g: Removing the Barriers
to Widespread Adoption of Support Vector Machines. Proceedings of the 31st VLDB Conference;
2005; Trondheim, Norway.
99. Zhang Z, Beck MW, Winkler DA, Huang B, Sibanda W, Goyal H, et al. Opening the black box of neural
networks: methods for interpreting neural network models in clinical applications. Ann Transl Med.
2018; 6(11):216. https://doi.org/10.21037/atm.2018.05.32 PMID: 30023379; PubMed Central PMCID:
PMCPMC6035992.
100. Fisher JM, Hammerla NY, Ploetz T, Andras P, Rochester L, Walker RW. Unsupervised home monitor-
ing of Parkinson’s disease motor symptoms using body-worn accelerometers. Parkinsonism Relat
Disord. 2016; 33:44–50. https://doi.org/10.1016/j.parkreldis.2016.09.009 PMID: 27637282.
101. Patel VL, Shortliffe EH, Stefanelli M, Szolovits P, Berthold MR, Bellazzi R, et al. The coming of age of
artificial intelligence in medicine. Artif Intell Med. 2009; 46(1):5–17. https://doi.org/10.1016/j.artmed.
2008.07.017 PMID: 18790621; PubMed Central PMCID: PMCPMC2752210.
102. Hersh W, Wright A. What workforce is needed to implement the health information technology
agenda? Analysis from the HIMSS analytics database. AMIA Annu Symp Proc. 2008: 303–7. PMID:
18998990; PubMed Central PMCID: PMCPMC2656033.
103. Abbas A, Zhang L, Khan SU. A survey on context-aware recommender systems based on computa-
tional intelligence techniques. Computing. 2015; 97:667–90.
104. Gutierrez PA, Hervas-Martinez C. Hybrid Artificial Neural Networks: Models, Algorithms and Data.
IWANN Berlin Heidelberg: Springer-Verlag; 2011. p. 177–94.
105. Fong S, Si YW, Biuk-Aghai RP. Applying a Hybrid Model of Neural Network and Decision Tree Classi-
fier for Predicting University Admission. IEEE2009.
106. Tkac M, Verner R. Artificial neural networks in business: Two decades of research. Applied Soft Com-
puting. 2016; 38:788–804.
107. Buttar HS, Rajan V. From $600 M to $6 Billion, Artificial Intelligence Systems Poised for Dramatic Mar-
ket Expansion in Healthcare. Frost & Sullivan, 2016.
108. Collier M, Fu R, Yin L, Christiansen P. Artificial Intelligence: Healthcare’s New Nervous System.
Accenture, 2017.
109. Reller R. AI’s revolutionary role in healthcare 2017 [cited 2017 1 December 2017]. Available from:
https://www.elsevier.com/connect/ais-revolutionary-role-in-healthcare.
110. Smith R. Artificial intelligence: coming soon to a hospital near you: STAT; 2017 [24 April 2018]. Avail-
able from: https://www.statnews.com/2017/04/13/artificial-intelligence-surgeons-hospital/.
111. Murdoch TB, Detsky AS. The inevitable application of big data to health care. JAMA. 2013; 309
(13):1351–2. https://doi.org/10.1001/jama.2013.393 PMID: 23549579.
112. Cockburn IM, Henderson R, Stern S. The Impact of Artificial Intelligence on Innovation: An Exploratory
Analysis. NBER Conference on Research Issues in Artificial Intelligence; Toronto2017.
113. McDaniel RR, Lanham HJ, Anderson RA. Implications of complex adaptive systems theory for the
design of reserch on health care organizations. Health Care Management Rev. 2009; 34(2):191–9.
114. Saadat P. A Complex Adaptive Systems Perspective to Appreciative Inquiry: A Theoretical Analysis.
Advances in Business Research. 2015; 6:1–13.
115. Paina L, Peters DH. Understanding pathways for scaling up health services through the lens of com-
plex adaptive systems. Health Policy Plan. 2012; 27(5):365–73. https://doi.org/10.1093/heapol/czr054
PMID: 21821667.
116. Plsek PE, Greenhalgh T. Complexity science: The challenge of complexity in health care. BMJ. 2001;
323(7313):625–8. PMID: 11557716; PubMed Central PMCID: PMCPMC1121189.
Applications of ANN in health care organizational decision-making: A scoping review
PLOS ONE | https://doi.org/10.1371/journal.pone.0212356 February 19, 2019 22 / 22
Copyright of PLoS ONE is the property of Public Library of Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
