Machine Learning and Artificial Intelligence for Medicine.





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van der Schaar Lab

This repository contains the implementations of algorithms developed by the van der Schaar Lab.

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An overview of the content of this repository is as below:

├── alg/        # Directory contains algorithms.
├── app/        # Directory contains apps.
├── cfg/        # Directory contains common config.
├── doc/        # Directory contains common docs.
├── init/       # Directory contains algorithms.
├── template/   # Directory contains templates.
└── util/       # Directory contains common utilities.


The publications and the corresponding locations in the repo are listed below:

Paper [Link]Journal/ConferenceCode
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes [Link]NIPS 2017alg/causal_multitask_gaussian_processes_ite
Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks [Link]NIPS 2017alg/dgp_survival
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning [Link]ICML 2018alg/autoprognosis
Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design [Link]ICML 2018alg/causal_multitask_gaussian_processes_ite
GAIN: Missing Data Imputation using Generative Adversarial Nets [Link]ICML 2018alg/gain
RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks [Link]ICML 2018alg/RadialGAN
GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets [Link]ICLR 2018alg/ganite
Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks [Link]ICLR 2018alg/DeepSensing (MRNN)
DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks [Link]AAAI 2018alg/deephit
INVASE: Instance-wise Variable Selection using Neural Networks [Link]ICLR 2019alg/invase
PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees [Link]ICLR 2019alg/pategan
KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks [Link]ICLR 2019alg/knockoffgan
ASAC: Active Sensing using Actor-Critic Models [Link]MLHC 2019alg/asac
Demystifying Black-box Models with Symbolic Metamodels [Link]NeurIPS 2019alg/symbolic_metamodeling
Differentially Private Bagging: Improved Utility and Cheaper Privacy than Subsample-and-Aggregate [Link]NeurIPS 2019alg/dpbag
Time-series Generative Adversarial Networks [Link]NeurIPS 2019alg/timegan
Attentive State-Space Modeling of Disease Progression [Link]NeurIPS 2019alg/attentivess
Conditional Independence Testing using Generative Adversarial Networks [Link]NeurIPS 2019alg/gcit
Dynamic-DeepHit: A Deep Learning Approach for Dynamic Survival Analysis with Competing Risks based on Longitudinal Data [Link]IEEEalg/dynamic_deephit
Temporal Quilting for Survival Analysis [Link]AISTATS 2019alg/survivalquilts
Estimating Counterfactual Treatment Outcomes over Time through Adversarially Balanced Representations [Link]ICLR 2020alg/counterfactual_recurrent_network
Contextual Constrained Learning for Dose-Finding Clinical Trials [Link]AISTATS 2020alg/c3t_budgets
Learning Overlapping Representations for the Estimation of Individualized Treatment Effects [Link]AISTATS 2020alg/dklite
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes [Link]AISTATS 2020alg/dynamic_disease_network_ddp
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning [Link]AISTATS 2020alg/smsdkl
Temporal Phenotyping using Deep Predicting Clustering of Disease Progression [Link]ICML 2020alg/ac_tpc
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders [Link]ICML 2020alg/time_series_deconfounder
Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions [Link]ICML 2020alg/discriminative-jackknife
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions [Link]ICML 2020alg/rnn-blockwise-jackknife
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [Link]ICML 2020alg/transductive_dropout
Anonymization Through Data Synthesis Using Generative Adversarial Networks (ADS-GAN) [Link]IEEEalg/adsgan
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes [Link]NeurIPS 2020alg/compartmental_gp
Strictly Batch Imitation Learning by Energy-based Distribution Matching [Link]NeurIPS 2020alg/edm
Gradient Regularized V-Learning for Dynamic Treatment Regimes [Link]NeurIPS 2020alg/grv
CASTLE: Regularization via Auxiliary Causal Graph Discovery [Link]NeurIPS 2020alg/castle
OrganITE: Optimal transplant donor organ offering using an individual treatment effect [Link]NeurIPS 2020alg/organite
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification [Link]NeurIPS 2020alg/r2p-hte
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks [Link]NeurIPS 2020alg/scigan
Learning outside the Black-Box: The pursuit of interpretable models [Link]NeurIPS 2020alg/Symbolic-Pursuit
VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain [Link]NeurIPS 2020alg/vime
Scalable Bayesian Inverse Reinforcement Learning [Link]ICLR 2021alg/scalable-birl
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms [Link]AISTATS 2021alg/CATENets
Learning Matching Representations for Individualized Organ Transplantation Allocation [Link]AISTATS 2021alg/MatchingRep
Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning [Link]ICLR 2021alg/interpole

Details of apps and other software is listed below:

App/Software [Link]DescriptionPublicationCode
Adjutorium COVID-19 [Link]Adjutorium COVID-19: an AI-powered tool that accurately predicts how COVID-19 will impact resource needs (ventilators, ICU beds, etc.) at the individual patient level and the hospital level-app/adjutorium-covid19-public
Clairvoyance [Link]Clairvoyance: A Pipeline Toolkit for Medical Time Series-clairvoyance repository
Hide-and-Seek Privacy Challenge [Link]Hide-and-Seek Privacy Challenge: Synthetic Data Generation vs. Patient Re-identification with Clinical Time-series DataNeurIPS 2020 competition trackapp/hide-and-seek


Please cite the the applicable papers and van der Schaar Lab repository if you use the software.


Copyright 2019-2021 van der Schaar Lab.

This software is released under the 3-Clause BSD license unless mentioned otherwise by the respective algorithms and apps.

Installation instructions

See individual algorithm and app directories for installation instructions.

See also doc/ for common installation instructions.

Tutorials and or examples

See individual algorithm and app directories for tutorials and examples.


Data files (as well as other large files such as saved models etc.) can be downloaded as per instructions in the DATA-*.md (see e.g. files found in the corresponding directories.

More info

For more information on the van der Schaar Lab’s work, visit our homepage.


See individual algorithm and app directories for references.

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