Abstract
IT is revolutionizing the healthcare industry. The benefits being realized could not be imagined a few decades ago. Healthcare Data Analytics (HDA) has enabled medical practitioners to perform prescriptive, descriptive and predictive analytics. This capability has rendered the practitioners far more effective and efficient as compared to their previous generations. At the same time, humankind is being served by the more meaningful diagnosis of diseases, better healthcare, more effective treatments and earlier detection of health issues. However, healthcare practitioners still rely on their expert judgement during emergency situations because there is no assurance of response time determinism (RTD) in current HDA systems. This paper addresses this problem by proposing the inclusion of RTD in HDAs using a recent technique developed in the field of real-time systems. An experiment was conducted simulating a life-saving scenario of this technique to demonstrate this concept. Time gains of up to 17 times were achieved, exhibiting promising results.
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Shah, S.A.B., Aziz, S.M. (2020). Response Time Determinism in Healthcare Data Analytics Using Machine Learning. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_23
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DOI: https://doi.org/10.1007/978-3-030-63820-7_23
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