School of Medicine
Showing 11-20 of 20 Results
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Curtis Langlotz
Professor of Radiology (Thoracic Imaging), of Biomedical Informatics Research and of Biomedical Data Science
Current Research and Scholarly InterestsI am interested in the use of deep neural networks and other machine learning technologies to help radiologists detect disease and eliminate diagnostic errors. My laboratory is developing deep neural networks that detect and classify disease on medical images. We also develop natural language processing methods that use the narrative radiology report to create large annotated image training sets for supervised machine learning experiments.
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Maya Mathur
Assistant 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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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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Sandy Napel
Professor of Radiology (Integrative Biomedical Imaging Informatics) and, by courtesy, of Medicine (Medical Informatics) and of Electrical Engineering
Current Research and Scholarly InterestsMy research seeks to advance the clinical and basic sciences in radiology, while improving our understanding of biology and the manifestations of disease, by pioneering methods in the information sciences that integrate imaging, clinical and molecular data. A current focus is on content-based radiological image retrieval and integration of imaging features with clinical and molecular data for diagnostic, prognostic, and therapy planning decision support.
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Natalie Pageler
Clinical Professor, Peds/Clinical Informatics
Clinical Professor, Medicine - Biomedical Informatics ResearchCurrent Research and Scholarly InterestsIn my administrative role, I oversee the development and maintenance of clinical decision support tools within the electronic medical record. These clinical decision support tools are designed to enhance patient safety, efficiency, and quality of care. My research focuses on rigorously evaluating--1) how these tools affect clinician knowledge, attitudes, and behaviors; and 2) how these tools affect clinical outcomes and efficiency of health care delivery.
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Jonathan P. Palma
Clinical Professor, Medicine - Biomedical Informatics Research
Current Research and Scholarly InterestsInterventional informatics to achieve examples of a learning healthcare system; optimization of commercial EMRs to support complex clinical workflows in newborn intensive care; clinical decision support; real-time clinical dashboards; electronic sign-out tools; IT-supported patient/family communication.
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Daniel Rubin
Professor of Biomedical Data Science, of Radiology (Integrative Biomedical Imaging Informatics at Stanford), of Medicine (Biomedical Informatics Research) and, by courtesy, of Ophthalmology
Current Research and Scholarly InterestsMy research interest is imaging informatics--ways computers can work with images to leverage their rich information content and to help physicians use images to guide personalized care. Work in our lab thus lies at the intersection of biomedical informatics and imaging science.
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Nigam H. Shah, MBBS, PhD
Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
Current Research and Scholarly InterestsWe analyze multiple types of health data (EHR, Claims, Wearables, Weblogs, and Patient blogs), to answer clinical questions, generate insights, and build predictive models for the learning health system.
