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ASSET Seminar: Statistical and Machine Learning for Electronic Health Records: Challenges and Opportunities, Qi Long (University of Pennsylvania)
March 1 at 12:00 PM - 1:30 PM
Electronic health records (EHRs) offer great promises in advancing clinical research and transforming learning health systems. However, complex, temporal EHRs are fraught with biases and present daunting analytical challenges that, if not addressed, can exacerbate health inequities. EHRs data, recorded at irregular time intervals with varying frequencies, are multi-modal and multi-scale including structured data such as labs and vitals, codified data such as diagnosis and procedure codes, and unstructured data such as doctor notes and pathology reports. They are typically incomplete and contain various data errors. What’s more, data gaps and errors in EHRs are often unequally distributed across patient groups: People with less access to care, often people of color or with lower socioeconomic status, tend to have more incomplete EHRs. In this talk, I will discuss these challenges and share my research group’s recent work on developing robust statistical and machine learning methods for addressing some of these challenges. Our experience has demonstrated that a trans-disciplinary health data science approach that involves collaboration between statisticians, informaticians, computer scientists, and physician scientists can accelerate innovation in harnessing the full power of EHRs to tackle complex real-world problems and exert meaningful impact in medicine. To this end, I will also discuss some open questions that present opportunities for future research and collaboration.
Perelman School of Medicine and Department of Computer Science, University of Pennsylvania
Qi Long, PhD is a professor in the Department of Biostatistics, Epidemiology and Informatics with a secondary appointment in the Department of Computer and Information Science at the University of Pennsylvania. He is the Director of the Penn Center for Cancer Data Science, and Associate Director of the Penn Institute for Biomedical Informatics. Dr. Long is an elected Fellow of AAAS and ASA, and an elected member of ISI.
Dr. Long’s research is centered around developing robust statistical and machine learning methods for advancing equitable, intelligent health and medicine, particularly through the analysis of large-scale health data, including, but not limited to, -omics, electronic health records, and imaging data. Most recently, his research has also branched into trustworthy data science including data privacy and algorithmic fairness. His methods research has been funded by NIH, PCORI, and NSF.
In addition, Dr. Long has provided leadership in biomedical research. He currently co-leads the Pre-medical Cancer Immunotherapy Network for Canine Trials (PRECINCT), part of NIH/NCI’s Cancer Moonshot Initiative. He has also directed the statistical and data coordinating center for national research networks and large-scale multi-site clinical studies such as the Risk Underlying Rural Areas Longitudinal (RURAL) Cohort Study funded by NIH/NHLBI. The rich, yet complex data from these large-scale studies present exciting opportunities for methods research.