Sontag Lab

cfrnet

Counterfactual Regression

co-llm

Co-LLM: Learning to Decode Collaboratively with Multiple Language Models

deepDiagnosis

A torch package for learning diagnosis models from temporal patient data.

dmm

Deep Markov Models

embeddings

Code for AMIA CRI 2016 paper "Learning Low-Dimensional Representations of Medical Concepts"

HealthKnowledgeGraph

Health knowledge graph for 157 diseases and 491 symptoms, learned from >270,000 patients' data

omop-learn

Python package for machine learning for healthcare using a OMOP common data model

prancer

Platform enabling Rapid Annotation for Clinical Entity Recognition

structuredinference

Structured Inference Networks for Nonlinear State Space Models

TabLLM

clinicalml/cfrnet

Counterfactual Regression

clinicalml/TabLLM

clinicalml/omop-learn

Python package for machine learning for healthcare using a OMOP common data model

clinicalml/co-llm

Co-LLM: Learning to Decode Collaboratively with Multiple Language Models

clinicalml/prancer

Platform enabling Rapid Annotation for Clinical Entity Recognition

clinicalml/sc-foundation-eval

Code for evaluating single cell foundation models scBERT and scGPT

clinicalml/human_ai_deferral

Human-AI Deferral Evaluation Benchmark (Learning to Defer) AISTATS23

clinicalml/cotrain-prompting

Code for co-training large language models (e.g. T0) with smaller ones (e.g. BERT) to boost few-shot performance

clinicalml/realhumaneval

clinicalml/ContextualAutocomplete_MLHC2020

Code for Contextual Autocomplete paper published in MLHC2020

clinicalml/learn-to-defer

Code for "Consistent Estimators for Learning to Defer to an Expert" (ICML 2020)

clinicalml/onboarding_human_ai

Onboarding Humans to work with AI: Algorithms to find regions and describe them in natural language that show how humans should collaborate with AI (NeurIPS23)

clinicalml/proxy-anchor-regression

Code for ICML 2021 paper "Regularizing towards Causal Invariance: Linear Models with Proxies" (ICML 2021)

clinicalml/teaching-to-understand-ai

Code and webpages for our study on teaching humans to defer to an AI

clinicalml/vae_ssl

Scalable semi-supervised learning with deep variational autoencoders

clinicalml/ml_mmrf

Machine Learning with data from the Multiple Myeloma Research Foundation

clinicalml/parametric-robustness-evaluation

Code for paper "Evaluating Robustness to Dataset Shift via Parametric Robustness Sets"

clinicalml/ief

Train pharmacodynamic deep generative models to model disease progression

clinicalml/active_learn_to_defer

Code for Sample Efficient Learning of Predictors that Complement Humans (ICML 2022)

clinicalml/large-scale-temporal-shift-study

Code for Large-Scale Study of Temporal Shift in Health Insurance Claims. Christina X Ji, Ahmed M Alaa, David Sontag. CHIL, 2023. https://arxiv.org/abs/2305.05087

clinicalml/finding-decision-heterogeneity-regions

Code for "Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance" at NeurIPS 2021 https://arxiv.org/abs/2110.14508

clinicalml/clinicalml-scBERT-NMI

analysis code to reproduce results in NMI submission

clinicalml/omop-variation

Tools to identify and evaluate heterogeneity in decision-making processes.

clinicalml/rct-obs-extrapolation

Code for paper, "Falsification before Extrapolation in Causal Effect Estimation"

clinicalml/rct-obs-falsification

Code for paper, "Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions." This repo is currently under construction. Check back later for end-to-end notebooks recreating the results of our AISTATS 2023 paper.

clinicalml/SCOPE

Codebase for SCOPE architecture.

clinicalml/seq_to_final_benchmark

Code for Seq-to-Final: A Benchmark for Tuning from Sequential Distributions to a Final Time Point. Christina X Ji, Ahmed M Alaa, David Sontag. https://arxiv.org/abs/2407.09642

clinicalml/t2dm_provider_variation_analysis

clinicalml/censored-falsification

clinicalml/wilds

A machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models.