Artificial Intelligence for Public Health In this talk, we examine artificial intelligence approaches for extracting actionable insights from health care data in order to improve public health. Our goal is to simultaneously identify subpopulations with distinct health risks and health trajectories and find the distinct risk factors or determinants associated each subpopulation. These determinants can then be used treatments, programs, and policies in order to reduce mortality and comorbidity and provide more efficient healthcare. We examine novel cadre machine learning approaches that combine predictive neural network modeling with more traditional statistical epidemiology methods for risk and survival analyses. We embed the cadre methods into a Semantically Targeted Analytics (Semantalytics) System that combines semantics, inference, automatic machine learning, and explainable AI. The AI system translate the public health questions to an analysis plan, prepares data, conducts analysis and reports results with visualization and text. We demonstrated these approach on public health care surveillance datasets and electronic medical records. The award winning xe2x80x9cMortalityMinderxe2x80x9d app examines the social determinants of xe2x80x9cDeaths Despairxe2x80x9d (deaths from suicide and substance abuse) and other causes of mortality that are unexpectedly rising in the United States. Other applications include association of environment toxins associated with diseases, high needs patient management for a health management organization, and emergency department readmissions. We conclude with the discussion of the open challenges to create population health AI systems that can transform health care questions into datadriven actionableinsights on the fly.

Supervised Mixture Models for Population Health We examine a machine learning approach for deriving insights from observational healthcare data in order to improve public health. Our goal is to simultaneously identify patient subpopulations with differing health risks and find the distinct risk factors or determinants associated with each subpopulation. Here, we develop a supervised Gaussian Mixture Model (GMM) approach for subpopulation modeling that combines GMMs with L1logistic regression. We demonstrate the approach on an analysis of high cost drivers of Medicaid expenditures for inpatient stays associated with Newborn, Pregnancy, and Circulatory Systems diagnostic categories. These conditions were chosen because they had the highest total inpatient expenditures in New York State (NYS) in 2016. When compared with stateoftheart learning methods (random forests, boosting, deep learning), our approach provides comparable prediction performance but also extracts insightful explanations of the subpopulation structure and risk factors within each subpopulation. Sequentially applying unsupervised learning methods and then applying logistic regression fails to yield equally meaningful results: the unsupervised subpopulations are homogeneous and moderately predictable, while some of our subpopulations are highly predictable with easytoidentify drivers of cost. Focusing on newborns, we unveil subpopulations indicative of the landscape of healthcare in NYS: about 90% of the discharges are healthy New York City babies and about 1% are costly complex cases. Subpopulations indicate regional disparities: for example newborns from Central, Southern and Western NY are of higher risk for highcost stays associated with substance abuse. The results indicate the promise of the approach for future population health studies based on electronic health care records.