Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Machine Learning and Artificial Intelligence for Medicine.
| Date | Stars |
|---|---|
| 2026-07-31 | 474 |
| 2026-08-05 | 473 |
| 2026-08-06 | 473 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# 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 [[L
Excerpt of 18,770 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:801c93b476ea6278, llm:Repository description: 'Machine Learning and Artificial Intelligence for Medicine'; topics: deep-learning, healthcare, machine-learning; README lists algorithms for treatment effect estimation, survival analysis, AutoPrognosis (automated clinical prognostic modeling), missing data imputation (GAIN) — domain applications in healthcare/medicine and ML algorithms.
matched fp:801c93b476ea6278, llm:Repository description: 'Machine Learning and Artificial Intelligence for Medicine'; topics: deep-learning, healthcare, machine-learning; README lists algorithms for treatment effect estimation, survival analysis, AutoPrognosis (automated clinical prognostic modeling), missing data imputation (GAIN) — domain applications in healthcare/medicine and ML algorithms.
matched fp:801c93b476ea6278, llm:Repository description: 'Machine Learning and Artificial Intelligence for Medicine'; topics: deep-learning, healthcare, machine-learning; README lists algorithms for treatment effect estimation, survival analysis, AutoPrognosis (automated clinical prognostic modeling), missing data imputation (GAIN) — domain applications in healthcare/medicine and ML algorithms.