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S. Joshua Swamidass*, Jonathan Chen*, Peter Phung, Jocelyne Bruand, Liva Ralaivola, and Pierre Baldi.
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NIH Big Data 2 Knowledge (BD2K) All Hands Meeting, November, 29, 2016 Pacific Symposium of Biocomputing, January 8, 2016 AMIA Joint Summits on Translational Science, San Francisco, CA, March 2017 (Student: Muthu Alaggapan)
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Columbia University Department of Biomedical Informatics, September 13, 2016
University of Pittsburgh Department of Biomedical Informatics, October 10, 2016
UCSF Division of Hospital Medicine, November 21, 2016
University of Washington Department of Biomedical Informatics and Medical Education, February 7, 2017
Washington University of St. Louis, Institute for Informatics, February 14, 2017
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Kaiser Permanente Division of Research, March 2016
Stanford Department of Medicine Grand Rounds, February 2016
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Veteran Affairs Palo Alto, Center for Innovation to Implementation (Ci2i), April 2015
Please do not use the contents of these presentations without express permission.
Jonathan H. Chen, M.D.,Ph.D.
Physician Data Scientist (Internal Medicine + Computer Science)
When medicine had relatively few effective interventions for patient care, it was possible for an individual clinician to know it all and do it all.
Experiential learning, heuristics, and pattern recognition became the norm, but collides with the current reality of an explosive growth in biomedical knowledge.
With thousands of medical and surgical procedures, diagnostic tests, pharmaceutical drugs, and recognized disease states, we routinely face a combinatorial explosion of possible medical decisions.
In the face of ever escalating complexity in medicine, integrating informatics solutions is the only credible approach to systematically address challenges in healthcare.
Tapping into real-world clinical data streams like electronic medical records with machine learning and data analytics will reveal the community's latent knowledge in a
reproducible form. Delivering this back to clinicians, patients, and healthcare systems as clinical decision support will uniquely close the loop on a continuously
learning health system. My group seeks to empower individuals with the collective experience of the many, combining human and artificial intelligence approaches to medicine
that will deliver better care than what either can do alone.
Reaction Explorer LLC, Founding Member (2010-Present)
Founding partner of startup company based on a unique system for
teaching complex problem-solving in organic chemistry
with the aid of expert system technology.
Original inventor of the technology from graduate research project
Carried the concept through from original invention to formation of the company
and translation of the technology into a profitable commercial application.
In partnership with John Wiley & Sons, Inc., global leader in higher education publishing,
the application is now being distributed to schools around the world
so that they may benefit from its unique learning advantages.
Medical Calculation / Analysis Tools (2009-Present)
Web-based scripts / pages for calculation and analysis
of some common issues on medicine wards.
Shivaal Roy - Undergraduate-Masters Co-terminal, Computer Science
Jason K Wang - Undergraduate, Math & CS
Gustavo Chavez - Research Student -> Medical Student, Stanford
Muthuraman Alagappan - Medical Student -> Internal Medicine Resident, Beth Israel-Deaconess
Healthcare politics and economics is about hard tradeoffs between the cost, quality, and access to healthcare.
The only way to improve all three simultaneously is science and technology to advance the frontiers of practice.
The world needs postdoctoral fellows and graduate students like you who are ready to tackle complex problems in healthcare through data science and decision support solutions.
You will have the opportunity to work in close collaboration with clinicians, scientists, and healthcare systems with access to
a deep clinical data warehouse (i.e., electronic medical records), broad population health data sources (e.g., national claims), and
professional development resources like (grant) writing workshops and clinical shadowing experiences.
Research topics can range from machine learning, designing, and evaluating clinical decision support content to
health outcomes and epidemiologic research on the implications of physician practice against challenging issues in
opioid prescribing, duty-hours restrictions, and end-of-life counseling.
The strongest applicants will have experience in one or more key interdisciplinary areas (not all are expected, that's the point of learning together):
Computer Science or Informatics:
Proficiency in programming and software development with a habit for robust unit testing.
Our group mainly develops software in a Python + SQL environment with R for additional statistical analysis.
For decision support prototype development, web-based user interface design and human-computer interaction testing experience will be valuable.
Statistics and Mathematics:
Machine learning methodology (supervised and unsupervised) and evaluation including discrimination vs. calibration measures and (hyper)parameter optimization through cross-validation.
Observational research methods including interpreting multivariate regression, missing data imputation, propensity score matching, and bootstrap simulations.
Biomedical / Healthcare Science:
Understanding of clinical decision making processes, healthcare quality metrics and financial incentives, decision support interfaces and pitfalls.
For postdoctoral applicants, a track record of complete research projects and well-written, peer reviewed papers is expected.
Specific responsibilities and research projects will be tuned to the career goals, technical strengths, and interests of the applicant.
Interested applicants should submit a CV, 2-3 references, and a brief career goal statement.