Algorithmic Approaches To Personalized Health Care Principal Investigators: I. Paschalidis and W. Adams at Boston Univ., collaborative with D. Bertsimas at MIT The US health care system is costly and highly inefficient, because it is geared towards the treatment of acute conditions in a hospital setting rather than focusing on prevention. Reform efforts are relying on the meaningful use of Electronic Health Records (EHRs), invariably seen as a key to improving efficiency. EHRs and the digitization of data from medical devices have the potential to aid prevention, automate decision making, and facilitate treatment. We describe two parallel projects that leverage EHRs towards such goals. In the first project we seek to efficiently predict hospitalization of patients due to heart related problems based on their available medical history. To that end, we have developed machine learning and optimization techniques. The second project aims at developing algorithms that predict the effect of specific medications and automatically make dosage decisions. We have focused on Bivalirudin -a critical but sparsely used direct thrombin inhibitor- and built a mathematical model that predicts its anti-coagulation effect and controls the drug infusion rate by using adaptive control laws. Abstract Prediction of Heart-Related Hospitalization Using Electronic Health Records Demographics(#5) Patient ID, Sex, Age, Race, Zip code Diagnoses (#22) E.g, Acute Myocardial Infarction, Heart Failure Procedures (#7) E.g, Cardiovascular Procedure, Vitals (#2) E.g, Diastolic/Systolic Blood Pressure Lab Tests (#4) E.g, Creatine Phosphokinase (CPK) Tobacco (#2) E.g, Cigarette Use Emergency (#1) E.g, Visit to Emergency Room Admission (#17) E.g, Heart Transplant, Coronary Bypass The Problem: The Data:  We select patients with heart-related problems during the period 01/01/ /31/2010.  For these patients, we extract their medical history during the period 01/01/ /31/2010: Preprocessing: Algorithms: Generative Methods Naïve Bayesian Model (Independent Features) Hidden Markov Model (Overall Patient Conditions) Discriminative Methods Adaboost with Trees (Combine Multiple Trees) Logistic Regression (Smooth Logistic Function) Support Vector Machines (Maximize Margins) Experimental Results Fig3. Model Reference Control Fig4. Controllable Plant Fig5. Output TrackingFig6. Tracking ErrorFig7. Control signal 1. Direct Adaptive control with known parameters 2. Indirect Adaptive control with unknown parameters 1.Regularized Regression Feature Selection Algorithm: Elastic Net Regression and Cross Validation Advantage: Method does not need the specific structure of system and have accurate prediction. Fig2. Dynamic model to simulate Bivalirudin elimination process. 3. Individual Model Adaptation Adapt Parameters by Extended Kalman Filter. Advantage: Accurate general model for population-wide patients; light computation task. Formulate as a nonlinear optimization to do parameter identification. Fig1. Multiple Time Series Input Regression Engine 2. Dynamic Model Based Parameter Identification (NLP ). Results Table 1st order polynomial 2 nd order polynomial NLPEKF RMSE σRMSE NRMSE21.44%36.58%26.42%18.03% σNRMSE6.33%9.82%11.04%6.18% Table1. Results Comparison Applying EKF algorithm with NLP solution as initial guess performs best. Algorithms could identify hospitalized patients at the cost of false alarms. E.g., two points (FA, MD) = (31.5%, 22.8%), (10,3%, 50%). Acknowledgments  Other contributors/collaborators: Wuyang Dai, Theodora Brisimi, Qi Zhao, Venkatesh Saligrama and Thomas Edrich.  This work was supported in part by the National Science Foundation under grant IIS Conclusions and Future Work  The model to predict future hospitalization could be incorporated into the EHR system and be used to trigger, for each risk cluster of patients, a set of necessary actions, including tests, additional measurements and physician involvement.  The mathematical model and method used to control Bivalirudin dosing may be useful to guide the optimal therapy for cardiac surgical patients and may provide a mathematical mechanism for development and testing of nomograms. Predicting the Effect of Bivalirudin Experimental Results How Do We Solve It? Results ComparisonModel Reference Adaptive Control (MRAC) 3. Direct Adaptive control with unknown parameters * The data comes from Brigham and Women's hospital. * The data comes from Boston Medical Center. Potential savings up to $19B per year