Nigam Shah
@drnigam
Faculty at Stanford Medicine
ID: 182130969
http://www.stanford.edu/~nigam 23-08-2010 22:27:37
253 Tweet
2,2K Followers
38 Following
Do ML models with better AUC performance necessarily have higher utility?🤔 Our new paper assesses utility of ML models for allocating interventions to avoid unplanned readmissions. Led by Michael Ko & Emma Chen Ashwin Agrawal w/ Nigam Shah et al. 1/n sciencedirect.com/science/articl…
Nice summary Jonathan Lu's work by Stanford HAI “Flying in the Dark”: Hospital AI Tools Aren’t Well Documented hai.stanford.edu/news/flying-da…
Thank you National Library of Medicine for supporting the #DataScience that led to the results in NEJM Catalyst. Nice example of how #informatics to drive innovations in #DigitalHealth delivery Stanford Health Care via a diverse team of Alison Callahan Saurabh Gombar Keith Morse Eli Cahan Robert Harrington + others!
Assessing the Future of #PublicHealth Data Exchange, #Interoperability ehrintelligence.com/news/assessing… “We need to rethink public health data [as a] fundamental construct,” Donald Rucker said. This involves shifting a mandated reporting mindset to a data reuse framework, he explained.
Results in NEJM Catalyst article, catalyst.nejm.org/doi/full/10.10….
Looking forward to the fun event by Karandeep Singh Yin Aphinyanaphongs on Nov 18th NYU Langone Health!
Superb signout by Stephen Pfohl! His is one of 179 papers accepted out of 711, completing his trilogy scholar.google.com/citations?view…, scholar.google.com/citations?view…, scholar.google.com/citations?view… on #algorithmic #fairness Stanford Medicine and Stanford HAI
Very thoughtful analysis Stanford Department of Medicine by Agata Foryciarz, Stephen Pfohl, Birju Patel on the interaction between imposing fairness constraints and practice guideline adherence. Good example of our holistic view at Stanford Medicine as suggested by Stanford HAI
“As a community, I think we’re hung up on the performance of the model and not asking the question, is the model useful? We need to think outside the model.” Nigam Shah, Chief Data Scientist for Stanford Health Care. stanford.io/3NXiKNk
#StanfordAIHealth is almost here! Join us Dec 6-7 to explore critical & emerging issues related to AI's impact across the spectrum of healthcare. Stellar program with talks, panels, firesides & live Q&A. 9 CME credits. Register: aihealth2022.stanford.edu Stanford HAI Stanford CME
Thank you Katie Link for sharing this work. In the spirit of the HELM project, crfm.stanford.edu/helm/latest/ Stanford HAI it would be amazing to get more focus on the need of "evaluations that matter" rather than "evaluations we can do"!
It was fun to summarize lessons learned from research in partnership with Stanford HAI, Center for Research on Foundation Models, Stanford Medicine for our clinical colleagues. We have to verify the claimed value propositions (hai.stanford.edu/news/how-found…) because they don't always pan out (hai.stanford.edu/news/how-well-…)!
Excited to be in Vancouver 🇨🇦 for #AAAI24 with Scott Fleming to present: "MedAlign: A Clinician-Generated Benchmark Dataset for Instruction Following with Electronic Medical Records" If you are interested in healthcare LLMs and preference alignment, checkout our talk and
Superb tweet chain by Jason Alan Fries reg the work on medalign.stanford.edu and why it matters. Check out the series 👇
