Assistant Professor
Medical Center Line
Center for Biomedical Informatics Research + Division of Hospital Medicine, Stanford Department of Medicine
https://profiles.stanford.edu/jonc101
Jonathan H Chen MD, PhD is a physician-scientist with professional software development experience and graduate training in computer science. He continues to practice Internal Medicine for the concrete rewards of caring for real people and to inspire his research focused on mining clinical data sources to inform medical decision making. He completed medical training in Internal Medicine and a VA Research Fellowship in Medical Informatics.
As an Assistant Professor of Medicine in the Stanford Center for Biomedical Informatics Research, Dr. Chen leads a research group that seeks to empower individuals with the collective experience of the many, combining human and artificial intelligence approaches that will deliver better care than either can alone. He has published influential work in venues including the New England Journal of Medicine, JAMA, JAMA Internal Medicine, Bioinformatics, Journal of Chemical Information and Modeling, and the Journal of the American Medical Informatics Association, with research awards and recognition from the NIH Big Data 2 Knowledge initiative, National Library of Medicine, the National Institute on Drug Abuse Clinical Trials Network, American Medical Informatics Association, Yearbook of Medical Informatics, and American College of Physicians, as well as the Stanford Artificial Intelligence in Medicine and Imaging – Human-Centered Artificial Intelligence (AIMI-HAI) Partnership Grant, among others.
SAGE Project: Prediction of Specialty Diagnostic Procedures for the Patients with Cognitive Impairment Diseases Using Deep Representation Learning of Electronic Health Record
Dementia is one of the major causes of mortality and morbidity in older people worldwide. However, dementia and mild cognitive impairment (MCI) are under-diagnosed. Early detection of cognitive decline may be critical to the efforts to stop dementia progression, including Alzheimer’s disease (AD) and AD-related dementias (ADRD). Early and accurate diagnosis of such diseases can be addressed by proposing new tools and models based on the patients’ medical records. In this project we use deep representation learning of the electronic health records (EHR) to predict patients who are likely to have early symptoms of cognitive impairment related diseases and need referral to specialists for further assessment as well as recommending the necessary specialty diagnostic procedures for these patients.
