About the Lab

Led by David Sontag, the Clinical Machine Learning Group is interested in advancing machine learning and artificial intelligence, and using these techniques to advance health care.

Broadly, we have two goals:

• Clinical: To truly make a difference in health care, we need to create algorithms that are useful for solving real clinical problems.
• Machine learning: We need rigorous solutions, which can pave the way for safe deployment of machine learning in high-stakes settings like healthcare.

News

• December 2022 Monica presented “Large Language Models are Few-Shot Clinical Information Extractors” at EMNLP 2022
• Nov. 2022: Three papers at NeurIPS 2022, including
  • Michael presenting “Evaluating Robustness to Dataset Shift via Parametric Robustness Sets”
  • Zeshan presenting “Falsification before Extrapolation in Causal Effect Estimation”
  • Hunter presenting “Training Subset Selection for Weak Supervision”
• July 2022: We have two papers at ICML 2022, with
  • Hunter and Monica presenting “Co-training improves prompt-based learning for large language models”
  • Hussein presenting “Sample Efficient Learning of Predictors that Complement Humans”
• March 2022: Members of our lab presented two papers at AISTATS 2022
  • “Leveraging Time Irreversibility with Order-Contrastive Pre-training “
  • “Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models “
• December 2021: We have two papers at AAAI 2022, with
  • Hussein presenting “Teaching Humans When To Defer to a Classifier via Exemplars”
  • Irene presenting “Clustering Interval-Censored Time-Series for Disease Phenotyping”
• October 2021: Luke presented MedKnowts: Unified Documentation and Information Retrieval for Electronic Health Records at UIST 2021. Explore the project page for a demo and more!
• August 2021: Jason presented Directing Human Attention in Event Localization for Clinical Timeline Creation at MLHC 2021
• July 2021: We have three papers at ICML 2021, with
  • Michael presenting “Regularizing towards Causal Invariance: Linear Models with Proxies”
  • Hunter presenting “Graph cuts always find a global optimum for Potts models (with a catch)”
  • Zeshan presenting “Neural Pharmacodynamic State Space Modeling”