School of Medicine
Showing 1-10 of 21 Results
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Russ B. Altman
Kenneth Fong Professor and Professor of Bioengineering, of Genetics, of Medicine (General Medical Discipline), of Biomedical Data Science and, by courtesy, of Computer Science
Current Research and Scholarly InterestsI refer you to my web page for detailed list of interests, projects and publications. In addition to pressing the link here, you can search "Russ Altman" on http://www.google.com/
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Jonathan H. Chen, MD, PhD
Assistant Professor of Medicine (Biomedical Informatics)
Current Research and Scholarly InterestsInformatics solutions ares the only credible approach to systematically address challenges of escalating complexity in healthcare. Tapping into real-world clinical data streams like electronic medical records will reveal the community's latent knowledge in a reproducible form. Delivering this back as clinical decision support will uniquely close the loop on a continuously learning health system.
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Deendayal Dinakarpandian
Clinical Associate Professor, Medicine - Biomedical Informatics Research
Current Research and Scholarly InterestsMethod development and insightful informatics based on my training as a physician, biochemist and computer scientist: Methods for representing, capturing and integrating emerging or expert biomedical knowledge to improve computational predictions of biological and clinical relevance. Methods for evaluating predictions based on machine learning. Interventional and causal predictions. Informatics research on problems in oncology, radiology and allergy/immunology.
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Norman Downing
Clinical Assistant Professor, Medicine - Biomedical Informatics Research
BioI am a faculty member in Biomedical Informatics Research at Stanford and board-certified internal medicine and clinical informatics. I split my time between clinical practice, hospital medical informatics and applications of artificial intelligence in healthcare. I work with the Clinical Excellence Research Center – a research group dedicated to reducing the cost of high-quality care – directing the Partnership in AI collaboration with the Stanford Artificial Intelligence Lab. Recognizing that the complexity of medicine has grown beyond the abilities of even the most expert clinician, we focus applications of computer vision to address some of the greatest challenges in healthcare: perfecting intended care for frail patients in settings ranging from the intensive care unit to the home. I have published work in the New England Journal of Medicine, Health Affairs, Annals of Internal Medicine, and the Journal of the American Medical Informatics Association. My interests include a design-based approach to understand how technology has impacted the work of clinicians and implications for new care models, workflow, and technology integration.
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Olivier Gevaert
Assistant Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
Current Research and Scholarly InterestsMy lab focuses on biomedical data fusion: the development of machine learning methods for biomedical decision support using multi-scale biomedical data. We primarily use methods based on regularized linear regression to accomplish this. We primarily focus on applications in oncology and neuroscience.
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Mary Kane Goldstein
Professor of Health Policy (PCOR) and, by courtesy, of Medicine (BMIR)
Current Research and Scholarly InterestsHealth services research in primary care and geriatrics: developing, implementing, and evaluating methods for clinical quality improvement. Current work includes applying health information technology to quality improvement through clinical decision support (CDS) integrated with electronic health records; encoding clinical knowledge into computable formats in automated knowledge bases; natural language processing of free text in electronic health records; analyzing multiple comorbidities
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Summer Han
Assistant Professor (Research) of Neurosurgery and of Medicine (Biomedical Informatics)
Current Research and Scholarly InterestsMy current research focuses on understanding the genetic and environmental etiology of complex disease and developing and evaluating efficient screening strategies based on etiological understanding. The areas of my research interests include statistical genetics, molecular epidemiology, cancer screening, health policy modeling, and risk prediction modeling. I have developed various statistical methods to analyze high-dimensional data to identify genetic and environmental risk factors and their interactions for complex disease.
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Zihuai He
Assistant Professor (Research) of Neurology and of Medicine (BMIR)
BioDr. He received his PhD from the University of Michigan in 2016. Following a postdoctoral training in biostatistics at Columbia University, he joined Stanford University as an assistant professor of neurology and of medicine in 2018. His research is concentrated in the area of statistical genetics and integrative analysis of omics data, with the aim of developing novel statistical and computational methodologies for the identification and interpretation of complex biological pathways involved in human diseases, particularly neurological disorders. His methodology interest includes high-dimensional data analysis, correlated (longitudinal, familial) data analysis and machine learning algorithms.
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Tina Hernandez-Boussard
Associate Professor of Medicine (Biomedical Informatics), of Biomedical Data Science, of Surgery and, by courtesy, of Epidemiology and Population Health
Current Research and Scholarly InterestsMy background and expertise is in the field of computational biology, with concentration in health services research. A key focus of my research is to apply novel methods and tools to large clinical datasets for hypothesis generation, comparative effectiveness research, and the evaluation of quality healthcare delivery. My research involves managing and manipulating big data, which range from administrative claims data to electronic health records, and applying novel biostatistical techniques to innovatively assess clinical and policy related research questions at the population level. This research enables us to create formal, statistically rigid, evaluations of healthcare data using unique combinations of large datasets.
