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Big Data Analytics in the E-Healthcare Industry

Knowledge Discovery and Information Interpretation

The Healthcare industry has various processes, including diagnosis, treatment, and prevention of diseases, injuries, and impairments in human beings. This industry is transforming at a great pace, and it is rich in data generated from a patient’s medical records, personal information, benchmarking findings, and administrative reports. These healthcare data are essential to the industry because they are a source of knowledge and valuable information required in the clinical practice. According to (Jothi et al. 2015), large volumes of data in the healthcare industry helps in the prediction of various diseases and assist doctors in diagnosis and making clinical decisions. Through the use of Internet of Things (IoT) devices, doctors obtain data that enable them to monitor personal health of their clients, model the spread of disease, and come up with measures to contain the outbreak of that disease (Dash et al., 2019). The IoT devices that generate large amounts of healthcare data include biosensors, health-tracking wearable devices, and devices used to monitor vital signs. The integration of these devices with electronic medical records and personal health records provide data that can be interpreted to understand a patient’s health status.  

However, there are significant challenges in knowledge discovery and information interpretation in big data analytics. According to (Ayani et al. 2019), the major challenge lies in the interpretation patterns of information after analysis. The use of Internet of Things devices generates large volumes of data that require the use of Machine Learning and Artificial intelligence to interpret. However, there has been a challenge of a simple representation of knowledge that has been extracted from big data (Ayani et al., 2019). It is challenging to develop and apply interpreted knowledge if it is not novel. Besides, there is a need for multidisciplinary expert teams to identify invalid patterns and accredit the knowledge extracted.

References

Ayani, S., Moulaei, K., Khanehsari, S. D., Jahanbakhsh, M., & Sadeghi, F. (2019). A Systematic Review of Big Data Potential to Make Synergies between Sciences for Achieving Sustainable Health: Challenges and Solutions. Applied Medical Informatics., 41(2), 53-64. Retrieved from: https://ami.info.umfcluj.ro/index.php/AMI/article/download/642/638

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0217-0

Jothi, N., Rashid, N. A., & Husain, W. (2015). Data Mining in Healthcare – A Review. Procedia Computer Science, 72, 306–313. https://doi.org/10.1016/j.procs.2015.12.145