The Development of Machine Learning Infused Outpatient Prognostic Models for tackling Impacts of Climate Change and ensuring Delivery of Effective Population Health Services
Proceedings - 2019 IEEE International Conference on Big Data
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. For example, Global warming cost the U.S. government more than 350 billion between 2007 and 2017 and will cost 112 billion per year in the future, according to the U.S. Government Accountability Office. Not only economic impact, global warming, importantly, is increasing the human fatality rates. 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. A clear example is a heat wave, scorching temperatures reached a record 116 degrees Fahrenheit, that struck the Indian city of Ahmedabad in 2010 killing hundreds of its most vulnerable citizens. The World Health Organization (WHO) estimates the climate change contributes to 150,000 deaths each year. By 2030, that number will double. 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. We strongly urge to include global warming related extreme events and its impacts on patients in Electronic Health Records (EHR) and to be imperatively considered as patient well-being data. Next, Electronic Health Records continuously synthesize relationship between Electronic Health Records (EHR) patient health episodes/encounters due to extreme climate events, albeit globally in a de-identified manner, and application of active machine learning techniques: A priori and naive Bayesian and artificial intelligence (AI) to derive hierarchical cohort of high risk senior citizens', outpatients, clusters and proactively delivering population health insights and delivery of timely health services through alerting governmental and non-governmental (NGO) agencies. The golden standard for success is to counter high temperature related mortality rates and to prevent deaths due to temperature related to global warming. In this research paper, we propose the development of high-risk temperature to high mortality cohort cluster-based Machine Learning (ML) / Artificial Intelligence (AI) algorithms to prevent deaths of senior citizens. Finally, the paper presents a cohort cluster prototype solution as well as its application and certain experimental results.
and Outpatient, Apriori Algorithm, Climate Change, EHR, Item set, Machine Learning, NOAA, Sanjeevani Electronic Health Records
Jaya Shankar Vuppalapati, Santosh Kedari, Anitha Ilapakurti, Chandrasekar Vuppalapati, Sharat Kedari, and Rajasekar Vuppalapati. "The Development of Machine Learning Infused Outpatient Prognostic Models for tackling Impacts of Climate Change and ensuring Delivery of Effective Population Health Services" Proceedings - 2019 IEEE International Conference on Big Data (2019): 2790-2799. https://doi.org/10.1109/BigData47090.2019.9006570