• Skip to primary navigation
  • Skip to main content
  • Skip to footer
Stanford Aging and Ethnogeriatrics Research Center (SAGE Center)

Stanford Aging and Ethnogeriatrics Research Center (SAGE Center)

Ace Aging

  • LinkedIn
  • Twitter
  • Home
  • About Us
  • Awardees
  • Faculty
  • Research Methodology Podcasts
  • Research
  • Contact Us

SAGE awardees

Suzanne Tamang, PhD

Instructor
Department of Biomedical Data Science

Dr. Tamang is the Assistant Faculty Director, Data Science, at the Stanford Center for Population Health Science and an Instructor at the Department of Biomedical Data Science. As a computer scientist with training in biology, health services research and biomedical informatics, Dr. Tamang works with interdisciplinary teams of experts on population health problems of public interest. Integral to her work, is the analysis of large and complex population-based datasets, using techniques from natural language processing, machine learning and deep learning. She brings extensive experience with US and Danish population-based registries, Electronic Medical Records from various vendors, administrative claims and other types of observational health data sources in the US and internationally; also, constructing, populating and applying knowledge-bases for automated reasoning. Dr. Tamang has developed open source tools for the extraction of health information from unstructured free-text patient notes and licensed machine learning prediction models to health analytics startups. Dr. Tamang affiliated with the Department of Veterans Affairs, the National Bureau of Economic Review, the Department of Rheumatology, University of California San Francisco and in Denmark, the Department of Clinical Epidemiology, Arhaus University. In addition to her more traditional research activities, she also functions a mentor for the Stanford working group Stats for Social Good. As a SAGE researcher, Dr. Tamang’s work will focus on opioid safety.

https://profiles.stanford.edu/suzanne-tamang

SAGE Project: Safer Opioid Prescribing for Vulnerable Populations and Developing the US Opioid Atlas

We are using Veterans Health Administration data to examine trends among veterans with a diagnoses opioid use disorder that experienced a Serious Adverse Event (e.g., non-fatal overdose and suicide attempts) with a focus on under represented minority older adults. Our analysis is based on the approximately eight million veterans that sought care at the VHA from 2016 through the end of 2018. Although our FDA collaborators have conducted similar work in other large population-based US datasets such as Optum and Truvan, their ability to characterize trends among under-represented minorities that are at high risk of opioids is limited. Our findings have the potential to improve mental healthcare management at the VA and patient-centered care initiatives at the FDA.

To help opioid investigators study these questions, we will develop a data visualization tool for exploring data on legal opioid ordering, by country and time, collected by Electronic Medical Record systems across 42 US States. Our design borrows ideas from the data visualization and multivariate statistics communities, especially the principles of linking and dimensionality reduction. Our work is relevant to policymakers and pain researchers who wish to systematically assess country-level factors that contribute to differences in opioid access for patients with cancer and surgery-related pain. Our tools. and the code behind it, will be freely available with an open source license.

Previous Post: « Alesha Heath, PhD
Next Post: Pros and Cons of Non-Inferiority Trials »

Ace Aging

Footer

Stanford Medicine

  • About
  • School Administration
  • Contact
  • Maps & Directions
  • Jobs

 

  • Basic Science Departments
  • Clinical Science Departments
  • Academic Programs
  • Diversity Programs

Healthcare

  • Find a physician
  • Clinical Trials
  • Patient Information
  • Contact

Related

  • Ethnogeriatrics
  • Salud (Spanish Health Site)
  • Project Respect
  •   Find People
  •   Visit Stanford
  •   Search Clinical Trials
  •   Give a Gift

Copyright © 2023 Stanford Medicine
Privacy Policy | Terms of Use

    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).