Donglin Zeng, Int. Tools from Stat Modelling and ML to Learn Optimal Treatment Regimes from EHR скачать в хорошем качестве

Donglin Zeng, Int. Tools from Stat Modelling and ML to Learn Optimal Treatment Regimes from EHR

Donglin Zeng, Professor, Biostatistics, University of North Carolina Integrating Tools from Statistical Modelling and Machine Learning to Learn Optimal Treatment Regimes from Electronic Health Records This talk presents a general framework to integrate analytic tools from both statistical modelling and machine learning to learn optimal treatment rules for type 2 diabetes (T2D) patients from electronic health records (EHRs). We first adopt joint statistical models to characterize patient's pretreatment conditions using longitudinal markers from EHRs. The statistical estimation accounts for informative measurement times using inverse intensity weighting methods. The predicted latent processes in the joint models are used to divide patients into a finite of subgroups and within each group, patients share similar health profiles in EHRs. Next, we learn optimal individualized treatment rules by extending a matched machine learning algorithm within each subgroup. We apply this integrative analysis to estimate optimal treatment rules for T2D patients in an EHRs dataset from the Ohio State University Wexner Medical Center. We demonstrate the utility of our method to select the optimal treatments from four classes of drugs and achieve a better control of glycated hemoglobin than any one-size-fits-all rules. Donglin Zeng obtained his PhD from Department of Statistics in the University of Michigan in 2001. Since then, he has been a faculty at Department of Biostatistics in the University of North Carolina. He is a fellow of both Institute of Mathematical Statistics and American Statistical Association. His research interest includes precision medicine, machine learning, electronic health records and high-dimensional data inference.