explainable AI, Data Analytics and Industrial Engineering Methods for Primary Care

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To tackle the chronic illnesses challenge, the applicant and his team introduced a new paradigm, referred to herein as Predict + Prevent, or P+P. Two important facts underlie this paradigm --- one is scientific and the other is historic. The scientific fact is that most chronic illnesses are actually preventable, if the appropriate lifestyle interventions are applied. The scientific evidence behind the preventability of chronic illnesses is overwhelming, as was reported during the last two decades in a number of articles in renowned medical journals. As for the historic fact, it is the result of a centuries-old paradigm we inherited from the way medicine (from Latin `medicina', which means `the art of healing') has traditionally been practiced: in general, patients need to wait until they get sick to go see their doctors who will then hopefully heal them. Now, with the availability of large sets of Electronic Medical Records data, and the tremendous advancements in information technology and data analytics, patients don't need to wait until they become sick. It is now possible to predict, at scale, the risk for a person to get ill with a chronic disease long before she contracts this disease. Hence the term "Predict" in the expression Predict + Prevent. The implementation of the P+P paradigm would consist of: (1) identify the high-risk patients, by predicting the likelihood for people to develop chronic diseases using EMR data, and then, (2) transfer lists of high-risk patients to a third-party, such as telehealth providers or human resources companies, who would then reach out to these patients (by e-mail, phone, social media, in-person or any other suitable method) and work with them on improving their lifestyle, to move them away from the onset of chronic diseases. In this proposal, the research will be focused mostly on the prediction side of the P+P paradigm and on limited aspects of prevention. More specifically, the proposed research project will address the following 5 projects listed below, using artificial intelligence methods and data analytics (e.g., decision trees, Markov chains with and without memory, Bayesian nets, mixture models, the EM algorithm, kernels and support vector machines, Monte Carlo simulations, unsupervised learning, and so on) for the first three projects, and industrial engineering methods (decision theory, engineering economics) for the last two projects: 1-Project on Prediction 1 - The single chronic disease cases 2-Project on Prediction 2 - Advanced stages and complications of a chronic disease 3-Project on Prediction 3 - Patients with multiple comorbidities 4-Project on Decision Sciences DS - Assessing the value of biomarkers information collected by doctors and their staff members. 5-Project on Engineering Economics EE - The economics of prevention This research will involve highly qualified personnel (HQP), namely 3 PhD students, 3 Master's students and 1 Lab technician.