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.

**12/5/2021**: Members of our lab presented two papers at AISTATS 2022: “Leveraging Time Irreversibility with Order-Contrastive Pre-training “ and “Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models “**12/5/2021**: We have two papers at AAAI 2022, with Hussein presenting “Teaching Humans When To Defer to a Classifier via Exemplars” and Irene presenting “Clustering Interval-Censored Time-Series for Disease Phenotyping”**10/12/2021**: Luke presented MedKnowts: Unified Documentation and Information Retrieval for Electronic Health Records at UIST 2021. Explore the project page for a demo and more!**8/06/2021**: Jason presented Directing Human Attention in Event Localization for Clinical Timeline Creation at MLHC 2021**7/19/2021**: We have three papers at ICML 2021, with Mike presenting “Regularizing towards Causal Invariance: Linear Models with Proxies”, Hunter presenting “Graph cuts always find a global optimum for Potts models (with a catch)", and Zeshan presenting “Neural Pharmacodynamic State Space Modeling”**5/13/2021**: Monica presented at CHI 2021 on assessing the impact of decision aid on clinicians**3/11/2021**: Watch David present “Using machine learning to guide treatment suggestions” at the European Winter Symposium on Machine Learning Frontiers in Precision Medicine**11/04/2020**: New paper in Science Translational Medicine on learning antibiotic treatment policies**11/04/2020**: We have released AMR-UTI through Physionet, a freely available dataset for studying antibiotic resistance and treatments**08/27/2020**: Mike presented at AISTATS 2020 on characterizing overlap in causal inference

We prove that the alpha-expansion algorithm for MAP inference always returns a globally optimal assignment for Markov Random Fields …

Modeling the time-series of high-dimensional, longitudinal data is important for predicting patient disease progression. However, …

We propose a method for learning linear models whose predictive performance is robust to causal interventions on unobserved variables, …

Healthcare providers are increasingly using machine learning to predict patient outcomes to make meaningful interventions. However, …

Overlap between treatment groups is required for non-parametric estimation of causal effects. If a subgroup of subjects always receives …

Quickly discover relevant content by filtering publications.

Unsupervised learning is often used to uncover clusters in data. However, different kinds of noise may impede the discovery of useful …

Label-scarce, high-dimensional domains such as healthcare present a challenge for modern machine learning techniques. To overcome the …

Expert decision makers are starting to rely on data-driven automated agents to assist them with various tasks. For this collaboration …