School of Medicine
Showing 41-50 of 78 Results
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Maya Mathur
Associate Professor (Research) of Pediatrics, of Medicine (Biomedical Informatics) and, by courtesy, of Epidemiology and Population Health
Current Research and Scholarly InterestsSynthesizing evidence across studies while accounting for biases
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Tushar Mungle
Postdoctoral Scholar, Biomedical Informatics
Current Research and Scholarly InterestsUse electronic health records (EHRs) to identify and classify common ocular diseases such as glaucoma, diabetic retinopathy, and macular degeneration. We aim to develop an approach to accurately identify these conditions using EHRs. This will be followed by cluster analysis to identify novel subtypes of these conditions that have not been recognized before. Finally, we will develop an approach to extract outcome data from EHRs for patients with these conditions in the primary care setting.
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Mark Musen
Stanford Medicine Professor of Biomedical Informatics Research, Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
Current Research and Scholarly InterestsModern science requires that experimental data—and descriptions of the methods used to generate and analyze the data—are available online. Our laboratory studies methods for creating comprehensive, machine-actionable descriptions both of data and of experiments that can be processed by other scientists and by computers. We are also working to "clean up" legacy data and metadata to improve adherence to standards and to facilitate open science broadly.
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Fateme Nateghi Haredasht
Postdoctoral Scholar, Biomedical Informatics
BioAs a postdoctoral scholar at the Stanford Center for Biomedical Informatics Research, I find myself at the exciting intersection of machine learning and healthcare. My journey began with a PhD in Biomedical Sciences from KU Leuven in Belgium, where I delved into the complexities of machine learning algorithms and their transformative potential in healthcare settings. My research, particularly focused on adapting these algorithms for time-to-event data (a method used for predicting specific events in a patient’s future), has not only been a challenging endeavor but also a deeply fulfilling one.
Now at Stanford, my role involves not just advancing machine learning integration in healthcare, but also collaborating with a diverse team of experts. Together, we're striving to unravel complex healthcare challenges and improve patient outcomes.
