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Stanford Aging and Ethnogeriatrics Research Center (SAGE Center)

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SAGE awardees

Jonathan H. Chen, MD, PhD

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.

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    Next-Generation Artificial Intelligence for Diagnosis: From Predicting Diagnostic Labels to "Wayfinding"

    Julia Adler-Milstein, PhD1; Jonathan H. Chen, MD, PhD; Gurpreet Dhaliwal, MD

    Improving the diagnostic process is a quality and safety priority.With the digitization of health records and rapid expansion of health data, the cognitive demand on the diagnostician has increased. The use of artificial intelligence (AI) to assist human cognition has the potential to reduce this demand and associated diagnostic errors. However, current AI tools have not realized this potential, due in part to the long-standing focus of these tools on predicting final diagnostic labels instead of helping clinicians navigate the dynamic refinement process of diagnosis. This Viewpoint highlights the importance of shifting the role of diagnostic AI from predicting labels to “wayfinding” (interpreting context and providing cues that guide the diagnostician).