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https://github.com/vanderschaarlab/mlforhealthlabpub

Machine Learning and Artificial Intelligence for Medicine.
https://github.com/vanderschaarlab/mlforhealthlabpub

deep-learning healthcare machine-learning

Last synced: 6 months ago
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Machine Learning and Artificial Intelligence for Medicine.

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README 

          
# van der Schaar Lab

__Note__ : For the most recent papers and code, checkout https://github.com/vanderschaarlab.



__Legacy code__ : This repository contains the implementations of algorithms developed

by the [van der Schaar Lab](https://www.vanderschaar-lab.com/) for papers before 2023.



## Content

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

```python

.

├── 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.

```



## Publications

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



Paper [[Link]](#) | Journal/Conference | Code

--- | --- | ---

Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes [[Link]](https://proceedings.neurips.cc/paper/2017/hash/6a508a60aa3bf9510ea6acb021c94b48-Abstract.html) | NIPS 2017 | [alg/causal_multitask_gaussian_processes_ite](alg/causal_multitask_gaussian_processes_ite)

Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks [[Link]](https://proceedings.neurips.cc/paper/2017/hash/861dc9bd7f4e7dd3cccd534d0ae2a2e9-Abstract.html) | NIPS 2017 | [alg/dgp_survival](alg/dgp_survival)

AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning [[Link]](https://icml.cc/Conferences/2018/Schedule?showEvent=2050) | ICML 2018 | [alg/autoprognosis](alg/autoprognosis)

Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design [[Link]](http://proceedings.mlr.press/v80/alaa18a.html) | ICML 2018 | [alg/causal_multitask_gaussian_processes_ite](alg/causal_multitask_gaussian_processes_ite)

GAIN: Missing Data Imputation using Generative Adversarial Nets [[Link]](http://proceedings.mlr.press/v80/yoon18a.html) | ICML 2018 | [alg/gain](alg/gain)

RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks [[Link]](http://proceedings.mlr.press/v80/yoon18b.html) | ICML 2018 | [alg/RadialGAN](alg/RadialGAN)

GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets [[Link]](https://openreview.net/forum?id=ByKWUeWA-) | ICLR 2018 | [alg/ganite](alg/ganite)

Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks [[Link]](https://openreview.net/forum?id=r1SnX5xCb) | ICLR 2018 | [alg/DeepSensing (MRNN)](alg/DeepSensing%20(MRNN))

DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks [[Link]](https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16160/15945) | AAAI 2018 | [alg/deephit](alg/deephit)

INVASE: Instance-wise Variable Selection using Neural Networks [[Link]](https://openreview.net/forum?id=BJg_roAcK7) | ICLR 2019 | [alg/invase](alg/invase)

PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees [[Link]](https://openreview.net/forum?id=S1zk9iRqF7) | ICLR 2019 | [alg/pategan](alg/pategan)

KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks [[Link]](https://openreview.net/forum?id=ByeZ5jC5YQ) | ICLR 2019 | [alg/knockoffgan](alg/knockoffgan)

ASAC: Active Sensing using Actor-Critic Models [[Link]](https://arxiv.org/abs/1906.06796) | MLHC 2019 | [alg/asac](alg/asac)

Demystifying Black-box Models with Symbolic Metamodels [[Link]](https://papers.nips.cc/paper/2019/hash/567b8f5f423af15818a068235807edc0-Abstract.html) | NeurIPS 2019 | [alg/symbolic_metamodeling](alg/symbolic_metamodeling)

Differentially Private Bagging: Improved Utility and Cheaper Privacy than Subsample-and-Aggregate [[Link]](https://papers.nips.cc/paper/2019/hash/5dec707028b05bcbd3a1db5640f842c5-Abstract.html) | NeurIPS 2019 | [alg/dpbag](alg/dpbag)

Time-series Generative Adversarial Networks [[Link]](https://papers.nips.cc/paper/2019/hash/c9efe5f26cd17ba6216bbe2a7d26d490-Abstract.html) | NeurIPS 2019 | [alg/timegan](alg/timegan)

Attentive State-Space Modeling of Disease Progression [[Link]](https://papers.nips.cc/paper/2019/hash/1d0932d7f57ce74d9d9931a2c6db8a06-Abstract.html) | NeurIPS 2019 | [alg/attentivess](alg/attentivess)

Conditional Independence Testing using Generative Adversarial Networks [[Link]](https://arxiv.org/abs/1907.04068) | NeurIPS 2019 | [alg/gcit](alg/gcit)

Dynamic-DeepHit: A Deep Learning Approach for Dynamic Survival Analysis with Competing Risks based on Longitudinal Data [[Link]](https://ieeexplore.ieee.org/document/8681104) | IEEE | [alg/dynamic_deephit](alg/dynamic_deephit)

Temporal Quilting for Survival Analysis [[Link]](http://proceedings.mlr.press/v89/lee19a.html) | AISTATS 2019 | [alg/survivalquilts](alg/survivalquilts)

Estimating Counterfactual Treatment Outcomes over Time through Adversarially Balanced Representations [[Link]](https://openreview.net/forum?id=BJg866NFvB) | ICLR 2020 | [alg/counterfactual_recurrent_network](alg/counterfactual_recurrent_network)

Contextual Constrained Learning for Dose-Finding Clinical Trials [[Link]](https://arxiv.org/abs/2001.02463) | AISTATS 2020 | [alg/c3t_budgets](alg/c3t_budgets)

Learning Overlapping Representations for the Estimation of Individualized Treatment Effects [[Link]](https://arxiv.org/abs/2001.04754) | AISTATS 2020 | [alg/dklite](alg/dklite)

Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes [[Link]](https://arxiv.org/abs/2001.02585) | AISTATS 2020 | [alg/dynamic_disease_network_ddp](alg/dynamic_disease_network_ddp)

Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning [[Link]](https://arxiv.org/abs/2001.03898) | AISTATS 2020 | [alg/smsdkl](alg/smsdkl)

Temporal Phenotyping using Deep Predicting Clustering of Disease Progression [[Link]](http://proceedings.mlr.press/v119/lee20h.html) | ICML 2020 | [alg/ac_tpc](alg/ac_tpc)

Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders [[Link]](http://proceedings.mlr.press/v119/bica20a.html) | ICML 2020 | [alg/time_series_deconfounder](alg/time_series_deconfounder)

Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions [[Link]](http://proceedings.mlr.press/v119/alaa20a.html) | ICML 2020 | [alg/discriminative-jackknife](alg/discriminative-jackknife)

Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions [[Link]](http://proceedings.mlr.press/v119/alaa20b.html) | ICML 2020 | [alg/rnn-blockwise-jackknife](alg/rnn-blockwise-jackknife)

Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [[Link]](http://proceedings.mlr.press/v119/chan20a.html) | ICML 2020 | [alg/transductive_dropout](alg/transductive_dropout)

Anonymization Through Data Synthesis Using Generative Adversarial Networks (ADS-GAN) [[Link]](https://ieeexplore.ieee.org/document/9034117) | IEEE | [alg/adsgan](alg/adsgan)

When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes [[Link]](https://vanderschaar-lab.com/papers/NeurIPS2020_CGP.pdf) | NeurIPS 2020 | [alg/compartmental_gp](alg/compartmental_gp)

Strictly Batch Imitation Learning by Energy-based Distribution Matching [[Link]](https://arxiv.org/abs/2006.14154) | NeurIPS 2020 | [alg/edm](alg/edm)

Gradient Regularized V-Learning for Dynamic Treatment Regimes [[Link]](https://vanderschaar-lab.com/papers/NeurIPS2020_GRV.pdf) | NeurIPS 2020 | [alg/grv](alg/grv)

CASTLE: Regularization via Auxiliary Causal Graph Discovery [[Link]](https://arxiv.org/abs/2009.13180) | NeurIPS 2020 | [alg/castle](alg/castle)

OrganITE: Optimal transplant donor organ offering using an individual treatment effect [[Link]](https://vanderschaar-lab.com/papers/NeurIPS2020_OrganITE.pdf) | NeurIPS 2020 | [alg/organite](alg/organite)

Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification [[Link]](https://arxiv.org/abs/2006.07917) | NeurIPS 2020 | [alg/r2p-hte](alg/r2p-hte)

Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks [[Link]](https://arxiv.org/abs/2002.12326) | NeurIPS 2020 | [alg/scigan](alg/scigan)

Learning outside the Black-Box: The pursuit of interpretable models [[Link]](https://arxiv.org/abs/2011.08596) | NeurIPS 2020 | [alg/Symbolic-Pursuit](alg/Symbolic-Pursuit)

VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain [[Link]](https://papers.nips.cc/paper/2020/hash/7d97667a3e056acab9aaf653807b4a03-Abstract.html) | NeurIPS 2020 | [alg/vime](alg/vime)

Scalable Bayesian Inverse Reinforcement Learning [[Link]](https://openreview.net/pdf?id=4qR3coiNaIv) | ICLR 2021 | [alg/scalable-birl](alg/scalable-birl)

Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms [[Link]](https://arxiv.org/abs/2101.10943) | AISTATS 2021 | [alg/CATENets](https://github.com/vanderschaarlab/CATENets)

Learning Matching Representations for Individualized Organ Transplantation Allocation [[Link]](https://arxiv.org/abs/2101.11769) | AISTATS 2021| [alg/MatchingRep](alg/MatchingRep)

Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning [[Link]](https://openreview.net/forum?id=unI5ucw_Jk) | ICLR 2021 | [alg/interpole](alg/interpole)

Inverse Decision Modeling: Learning Interpretable Representations of Behavior [[Link]](http://proceedings.mlr.press/v139/jarrett21a.html) | ICML 2021 | [alg/ibrc](alg/ibrc)

Policy Analysis using Synthetic Controls in Continuous-Time [[Link]](http://proceedings.mlr.press/v139/bellot21a/bellot21a.pdf) | ICML 2021 | [alg/Synthetic-Controls-in-Continuous-Time](https://github.com/vanderschaarlab/Synthetic-Controls-in-Continuous-Time/)

Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis [[Link]](http://proceedings.mlr.press/v139/berrevoets21a/berrevoets21a.pdf) | ICML 2021 | [alg/organsync](https://github.com/vanderschaarlab/organsync/)

Explaining Time Series Predictions with Dynamic Masks [[Link]](http://proceedings.mlr.press/v139/crabbe21a.html) | ICML 2021 | [alg/Dynamask](https://github.com/vanderschaarlab/Dynamask/)

Generative Time-series Modeling with Fourier Flows [[Link]](https://openreview.net/forum?id=PpshD0AXfA) | ICLR 2021 | [alg/Fourier-flows](https://github.com/vanderschaarlab/Fourier-flows/)

On Inductive Biases for Heterogeneous Treatment Effect Estimation [[Link]](https://arxiv.org/pdf/2106.03765.pdf) | NeurIPS 2021 | [alg/CATENets](https://github.com/vanderschaarlab/CATENets/)

Really Doing Great at Estimating CATE? A Critical Look at ML Benchmarking Practices in Treatment Effect Estimation [[Link]](https://openreview.net/pdf?id=FQLzQqGEAH) | NeurIPS 2021 | [alg/CATENets](https://github.com/vanderschaarlab/CATENets/)

The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation [[Link]](https://arxiv.org/abs/2106.04240) | NeurIPS 2021 | [alg/medkit-learn](https://github.com/vanderschaarlab/medkit-learn)

MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms [[Link]](https://neurips.cc/Conferences/2021/ScheduleMultitrack?event=27670) | NeurIPS 2021 | [alg/MIRACLE](https://github.com/vanderschaarlab/MIRACLE)

DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks [[Link]](https://neurips.cc/Conferences/2021/ScheduleMultitrack?event=27552) | NeurIPS 2021 | [alg/DECAF](https://github.com/vanderschaarlab/DECAF)

Explaining Latent Representations with a Corpus of Examples [[Link]](https://arxiv.org/abs/2110.15355) | NeurIPS 2021 | [alg/Simplex](https://github.com/vanderschaarlab/Simplex)

Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation [[Link]](https://neurips.cc/Conferences/2021/ScheduleMultitrack?event=26815) | NeurIPS 2021 | [alg/iTransplant](https://github.com/vanderschaarlab/iTransplant)

Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression [[Link]](https://papers.neurips.cc/paper/2021/hash/5ea1649a31336092c05438df996a3e59-Abstract.html) | NeurIPS 2021 | [alg/Hybrid-ODE-NeurIPS-2021](https://github.com/vanderschaarlab/Hybrid-ODE-NeurIPS-2021)

SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes [[Link]](https://proceedings.neurips.cc/paper/2021/hash/19485224d128528da1602ca47383f078-Abstract.html) | NeurIPS 2021 | [alg/SyncTwin-NeurIPS-2021](https://github.com/vanderschaarlab/SyncTwin-NeurIPS-2021)

Conformal Time-series Forecasting [[Link]](https://proceedings.neurips.cc/paper/2021/hash/312f1ba2a72318edaaa995a67835fad5-Abstract.html) | NeurIPS 2021 | [alg/conformal-rnn](https://github.com/vanderschaarlab/conformal-rnn/tree/master)

Estimating Multi-cause Treatment Effects via Single-cause Perturbation [[Link]](https://proceedings.neurips.cc/paper/2021/hash/c793b3be8f18731f2a4c627fb3c6c63d-Abstract.html) | NeurIPS 2021 | [alg/Single-Cause-Perturbation-NeurIPS-2021](https://github.com/vanderschaarlab/Single-Cause-Perturbation-NeurIPS-2021/)

Invariant Causal Imitation Learning for Generalizable Policies [[Link]](https://papers.nips.cc/paper/2021/file/204904e461002b28511d5880e1c36a0f-Paper.pdf) | NeurIPS 2021 | [alg/Invariant-Causal-Imitation-Learning](https://github.com/vanderschaarlab/Invariant-Causal-Imitation-Learning/tree/main)

Inferring Lexicographically-Ordered Rewards from Preferences [[Link]](https://ojs.aaai.org/index.php/AAAI/article/view/20516) | AAAI 2022 | [alg/lori](https://github.com/vanderschaarlab/lori)

Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies [[Link]](https://openreview.net/forum?id=DYypjaRdph2) | ICLR 2022 | [alg/inverse-online](https://github.com/vanderschaarlab/inverse-online)

D-CODE: Discovering Closed-form ODEs from Observed Trajectories [[Link]](https://openreview.net/forum?id=wENMvIsxNN) | ICLR 2022 | [alg/D-CODE-ICLR-2022](https://github.com/vanderschaarlab/D-CODE-ICLR-2022)

Neural graphical modelling in continuous-time: consistency guarantees and algorithms [[Link]](https://openreview.net/forum?id=SsHBkfeRF9L) | ICLR 2022 | [alg/Graphical-modelling-continuous-time](https://github.com/vanderschaarlab/Graphical-modelling-continuous-time)

Label-Free Explainability for Unsupervised Models [[Link]](https://proceedings.mlr.press/v162/crabbe22a) | ICML 2022 | [alg/Label-Free-XAI](https://github.com/vanderschaarlab/Label-Free-XAI)

Inverse Contextual Bandits: Learning How Behavior Evolves over Time [[Link]](https://proceedings.mlr.press/v162/huyuk22a.html) | ICML 2022 | [alg/invconban](https://github.com/vanderschaarlab/invconban)

Data-SUITE: Data-centric identification of in-distribution incongruous examples [[Link]](https://proceedings.mlr.press/v162/seedat22a.html) | ICML 2022 | [alg/Data-SUITE](https://github.com/vanderschaarlab/Data-SUITE)

Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations [[Link]](https://proceedings.mlr.press/v162/seedat22b) | ICML 2022 | [alg/TE-CDE](https://github.com/vanderschaarlab/TE-CDE)

Concept Activation Regions: A Generalized Framework For Concept-Based Explanations[[Link]](https://arxiv.org/abs/2209.11222) | NeurIPS 2022 | [alg/CARs](https://github.com/vanderschaarlab/CARs)

Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability[[Link]](https://arxiv.org/abs/2206.08363) | NeurIPS 2022 | [alg/ITErpretability](https://github.com/vanderschaarlab/ITErpretability)

Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation[[Link]](https://arxiv.org/abs/2210.06183) | NeurIPS 2022 | [alg/HTCE-learners](https://github.com/vanderschaarlab/HTCE-learners)

Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning[[Link]](https://arxiv.org/abs/2210.05320) | NeurIPS 2022 | [alg/HTCE-learners](https://github.com/vanderschaarlab/synthetic-model-combination)





Details of apps and other software is listed below:



App/Software [[Link]](#) | Description | Publication | Code

--- | --- | --- | ---

Adjutorium COVID-19 [[Link]](https://www.vanderschaar-lab.com/paper-on-covid-19-hospital-capacity-planning-published-in-machine-learning/) | 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](app/adjutorium-covid19-public)

Clairvoyance [[Link]](https://www.vanderschaar-lab.com/clairvoyance-alpha-the-first-unified-end-to-end-automl-pipeline-for-time-series-data/) | Clairvoyance: A Pipeline Toolkit for Medical Time Series | [ICML 2021](https://openreview.net/forum?id=xnC8YwKUE3k) | [clairvoyance repository](https://github.com/vanderschaarlab/clairvoyance)

Clairvoyance2 [[Link]](https://github.com/vanderschaarlab/clairvoyance2) | `clairvoyance2`: a Unified Toolkit for Medical Time Series | - | [clairvoyance2 repository](https://github.com/vanderschaarlab/clairvoyance2)

Hide-and-Seek Privacy Challenge [[Link]](http://www.vanderschaar-lab.com/privacy-challenge/) | Hide-and-Seek Privacy Challenge: Synthetic Data Generation vs. Patient Re-identification with Clinical Time-series Data | [NeurIPS 2020 competition track](https://arxiv.org/abs/2007.12087) | [app/hide-and-seek](app/hide-and-seek)



## Citations

Please cite the *the applicable papers* and [van der Schaar Lab repository](https://github.com/vanderschaarlab/mlforhealthlabpub/) if you use the software.



## Breakdown by category



**See breakdown [here](https://github.com/vanderschaarlab/.github/tree/main/profile).**



## License

Copyright **2019-2022** van der Schaar Lab.



This software is released under the [3-Clause BSD license](https://opensource.org/licenses/BSD-3-Clause) unless mentioned otherwise by the respective algorithms and apps.



## Installation instructions

*See individual algorithm and app directories for installation instructions.*



See also [doc/install.md](doc/install.md) for common installation instructions.



## Tutorials and or examples

*See individual algorithm and app directories for tutorials and examples.*



## Data

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. [DATA-PUBLIC.md](./DATA-PUBLIC.md)) files found in the corresponding directories.



## More info

For more information on the van der Schaar Lab’s work, visit [our homepage](https://www.vanderschaar-lab.com/).



## References

*See individual algorithm and app directories for references.*