no code implementations • 20 Nov 2023 • Lin Lawrence Guo, Jason Fries, Ethan Steinberg, Scott Lanyon Fleming, Keith Morse, Catherine Aftandilian, Jose Posada, Nigam Shah, Lillian Sung
With continued pretraining on local data, label efficiency substantially improved, such that $FM_{SM}$ required fewer than 1% of training examples to match the fully trained GBM's performance.
no code implementations • 24 Oct 2023 • Alejandro Lozano, Scott L Fleming, Chia-Chun Chiang, Nigam Shah
The quickly-expanding nature of published medical literature makes it challenging for clinicians and researchers to keep up with and summarize recent, relevant findings in a timely manner.
1 code implementation • 9 Jan 2023 • Ethan Steinberg, Jason Fries, Yizhe Xu, Nigam Shah
MOTOR is the first foundation model for medical TTE predictions and we release a 143M parameter pretrained model for research use at [redacted URL].
no code implementations • 23 Nov 2021 • Yuyin Zhou, Shih-Cheng Huang, Jason Alan Fries, Alaa Youssef, Timothy J. Amrhein, Marcello Chang, Imon Banerjee, Daniel Rubin, Lei Xing, Nigam Shah, Matthew P. Lungren
Despite the routine use of electronic health record (EHR) data by radiologists to contextualize clinical history and inform image interpretation, the majority of deep learning architectures for medical imaging are unimodal, i. e., they only learn features from pixel-level information.
1 code implementation • 15 Nov 2021 • Steve Yadlowsky, Scott Fleming, Nigam Shah, Emma Brunskill, Stefan Wager
We propose rank-weighted average treatment effect (RATE) metrics as a simple and general family of metrics for comparing and testing the quality of treatment prioritization rules.
1 code implementation • 17 Mar 2020 • Somalee Datta, Jose Posada, Garrick Olson, Wencheng Li, Ciaran O'Reilly, Deepa Balraj, Joseph Mesterhazy, Joseph Pallas, Priyamvada Desai, Nigam Shah
The ecosystem is designed to bring the modern data science community to highly sensitive clinical data in a secure and collaborative big data analytics environment with a goal to enable bigger, better and faster science.
no code implementations • 11 Dec 2019 • Anitha Kannan, Jason Alan Fries, Eric Kramer, Jen Jen Chen, Nigam Shah, Xavier Amatriain
A third of adults in America use the Internet to diagnose medical concerns, and online symptom checkers are increasingly part of this process.
1 code implementation • 16 Nov 2019 • Scott L. Fleming, Kuhan Jeyapragasan, Tony Duan, Daisy Ding, Saurabh Gombar, Nigam Shah, Emma Brunskill
There is an emerging trend in the reinforcement learning for healthcare literature.
3 code implementations • 14 Apr 2018 • Alejandro Schuler, Michael Baiocchi, Robert Tibshirani, Nigam Shah
Instead of relying on a single method, multiple models fit by a diverse set of algorithms should be evaluated against each other using an objective function learned from the validation set.
no code implementations • 31 Oct 2017 • Alejandro Schuler, Ken Jung, Robert Tibshirani, Trevor Hastie, Nigam Shah
Using simulations, we show that using synth-validation to select a causal inference method for each study lowers the expected estimation error relative to consistently using any single method.
no code implementations • 19 May 2017 • Sebastien Dubois, Nathanael Romano, David C. Kale, Nigam Shah, Kenneth Jung
We used the learned representations, along with commonly used bag of words and topic model representations, as features for predictive models of clinical events.