# Probabilistic Machine Learning for Healthcare

arXiv: https://arxiv.org/abs/2009.11087 · HTML: https://mlsanity.com/paper/2009.11087/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2020.

## Built on by (most cited first, 23 of 23)

- [Predictive Multiplicity in Probabilistic Classification](https://mlsanity.com/paper/2206.01131/) (2022)
- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [On the Challenges and Opportunities in Generative AI](https://mlsanity.com/paper/2403.00025/) (2024)
- [Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities](https://mlsanity.com/paper/2411.01053/) (2024)
- [Test-time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift](https://mlsanity.com/paper/2207.00769/) (2022)
- [Remote patient monitoring using artificial intelligence: Current state, applications, and challenges](https://mlsanity.com/paper/2301.10009/) (2023)
- [Quantifying disparities in intimate partner violence: a machine learning method to correct for underreporting](https://mlsanity.com/paper/2110.04133/) (2021)
- [FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records](https://mlsanity.com/paper/2505.16941/) (2025)
- [Offline Estimation of Controlled Markov Chains: Minimaxity and Sample Complexity](https://mlsanity.com/paper/2211.07092/) (2022)
- [Adaptive Multi-Agent Deep Reinforcement Learning for Timely Healthcare Interventions](https://mlsanity.com/paper/2309.10980/) (2023)
- [A Survey on Preserving Fairness Guarantees in Changing Environments](https://mlsanity.com/paper/2211.07530/) (2022)
- [A generative, predictive model for menstrual cycle lengths that accounts for potential self-tracking artifacts in mobile health data](https://mlsanity.com/paper/2102.12439/) (2021)
- [A Review on Oracle Issues in Machine Learning](https://mlsanity.com/paper/2105.01407/) (2021)
- [Some performance considerations when using multi-armed bandit algorithms in the presence of missing data](https://mlsanity.com/paper/2205.03820/) (2022)
- [HypUC: Hyperfine Uncertainty Calibration with Gradient-boosted Corrections for Reliable Regression on Imbalanced Electrocardiograms](https://mlsanity.com/paper/2311.13821/) (2023)
- [Central Limit Theorems for Transition Probabilities of Controlled Markov Chains](https://mlsanity.com/paper/2508.01517/) (2025)
- [PDRL: Multi-Agent based Reinforcement Learning for Predictive Monitoring](https://mlsanity.com/paper/2309.10576/) (2023)
- [Nonparametric modeling of the composite effect of multiple nutrients on blood glucose dynamics](https://mlsanity.com/paper/2311.03129/) (2023)
- [Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis](https://mlsanity.com/paper/2407.11427/) (2024)
- [Time After Time: Deep-Q Effect Estimation for Interventions on When and What to do](https://mlsanity.com/paper/2503.15890/) (2025)
- [DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection](https://mlsanity.com/paper/2506.10200/) (2025)
- [Prediction of Hospital Associated Infections During Continuous Hospital Stays](https://mlsanity.com/paper/2508.13561/) (2025)
- [Neuron Incidence Redistribution for Fairness in Medical Image Classification](https://mlsanity.com/paper/2605.19393/) (2026)

## Abstract (arXiv)

Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance healthcare. We consider challenges in the predictive model building pipeline where probabilistic models can be beneficial including calibration and missing data. Beyond predictive models, we also investigate the utility of probabilistic machine learning models in phenotyping, in generative models for clinical use cases, and in reinforcement learning.