Human-AI symbiosis: Decode climate change to prevent heat-related mortalities and to protect our most vulnerable population

Publication Date

8-1-2019

Document Type

Conference Proceeding

Publication Title

Proceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019

DOI

10.1109/CSE/EUC.2019.00070

First Page

331

Last Page

338

Abstract

Climate change is challenging our way of lives. Rising global temperatures are triggering increases in the frequency and severity of extreme climatic events, such as floods, droughts, & heat waves and resulting in unprecedented increase in economic cost and human impact. The environmental and health research clearly suggest the linkage between global warming and increase in heat-related mortality, particularly in low-latitude countries, such as India, where heat waves will become more frequent and populations are especially vulnerable to these extreme temperatures. The dire consequence of global warming and increase temperature mortality rates is unequivocally faced by the most vulnerable population, Senior Citizens, due to lack of proactive insights and timely availability of healthcare services. Our groundbreaking and innovative proposal is to continuously synthesize most venerable senior citizens medical & diagnostic Electronic Health Records (EHR) data to identify highly susceptible senior citizens to heat waves so as proactively deliver actionable recommendations to family and primary care medical members to prevent negative effects of heat-waves.

Keywords

Apriori Algorithm, Cluster, Electronic Health Records, Electronic Health Records, Hanumayamma Innovations and Technologies, Inc, Item set, Machine Learning, Naïve Bayesian, Outpatient, Sanjeevani EHR

Department

Computer Engineering

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