165 citations · 329 across the 17 of their papers we have counts for
16 papers · 1 filter
Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance
Justin Lim, Christina X Ji, Michael Oberst +3
Individuals often make different decisions when faced with the same context, due to personal preferences and background. For instance, judges may vary in their leniency towards cer…
Regularizing towards Causal Invariance: Linear Models with Proxies
Michael Oberst, Nikolaj Thams, Jonas Peters +1
We propose a method for learning linear models whose predictive performance is robust to causal interventions on unobserved variables, when noisy proxies of those variables are ava…
Neural Pharmacodynamic State Space Modeling
Zeshan Hussain, Rahul G. Krishnan, David Sontag
Modeling the time-series of high-dimensional, longitudinal data is important for predicting patient disease progression. However, existing neural network based approaches that lear…
Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning Studies
Christina X. Ji, Michael Oberst, Sanjat Kanjilal +1
Reinforcement learning (RL) has the potential to significantly improve clinical decision making. However, treatment policies learned via RL from observational data are sensitive to…
Fast, Structured Clinical Documentation via Contextual Autocomplete
Divya Gopinath, Monica Agrawal, Luke Murray +3
We present a system that uses a learned autocompletion mechanism to facilitate rapid creation of semi-structured clinical documentation. We dynamically suggest relevant clinical co…
Deep Contextual Clinical Prediction with Reverse Distillation
Rohan S. Kodialam, Rebecca Boiarsky, Justin Lim +3
Healthcare providers are increasingly using machine learning to predict patient outcomes to make meaningful interventions. However, despite innovations in this area, deep learning…