Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?

1 code implementation • 9 Nov 2023 • Walter Nelson, Jonathan Ranisau, Jeremy Petch

We began by empirically evaluating 6 modern machine learning-based outlier detection algorithms on the task of identifying irregular data in 838 datasets from 7 real-world MCRCTs with a total of 77, 001 patients from over 44 countries.

Meta-Learning Model Selection +1

Pyclipse, a library for deidentification of free-text clinical notes

no code implementations • 5 Nov 2023 • Callandra Moore, Jonathan Ranisau, Walter Nelson, Jeremy Petch, Alistair Johnson

Automated deidentification of clinical text data is crucial due to the high cost of manual deidentification, which has been a barrier to sharing clinical text and the advancement of clinical natural language processing.

Semi-Markov Offline Reinforcement Learning for Healthcare

1 code implementation • 17 Mar 2022 • Mehdi Fatemi, Mary Wu, Jeremy Petch, Walter Nelson, Stuart J. Connolly, Alexander Benz, Anthony Carnicelli, Marzyeh Ghassemi

Finally, we apply our new algorithms to a real-world offline dataset pertaining to warfarin dosing for stroke prevention and demonstrate similar results.

Offline RL reinforcement-learning +1