InterventionAware Early Warning How can we early warn against an impending student drop out or an adverse health condition in near realtime? More challengingly, how do we learn to early warn from data containing confounding interventionse.g., tutoring or medicineswhile remaining interpretable to the human decision maker? We consider the problem of learning to interpretably early warn from labeled data tainted by interventions. We first identify three principles that an early warning system should follow. We then propose SmokeAlarm which provably obeys these principles and produces early warning scores in an online manner. Notably, learned model is biinspectable, i.e., it can be visualized both in the presence and in the absence of interventions. Experiments demonstrate the efficacy of SmokeAlarm over prior approaches.

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.