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Probabilistic Machine Learning for Healthcare

arXiv 2009.11087 ↗First posted 23 Sep 2020stat.ML cs.CY cs.LGcited by 23

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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.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Sep 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 5; 2023: 5; 2024: 3; 2025: 5; 2026: 1.

23 papers.

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  1. 2022Predictive Multiplicity in Probabilistic Classificationcited by 44
  2. 2021Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Reviewcited by 35
  3. 2024On the Challenges and Opportunities in Generative AIcited by 31
  4. 2024Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalitiescited by 23
  5. 2022Test-time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shiftcited by 20
  6. 2023Remote patient monitoring using artificial intelligence: Current state, applications, and challengescited by 14
  7. 2021Quantifying disparities in intimate partner violence: a machine learning method to correct for underreportingcited by 8
  8. 2025FoMoH: A clinically meaningful foundation model evaluation for structured electronic health recordscited by 6
  9. 2022Offline Estimation of Controlled Markov Chains: Minimaxity and Sample Complexitycited by 5
  10. 2023Adaptive Multi-Agent Deep Reinforcement Learning for Timely Healthcare Interventionscited by 3
  11. 2022A Survey on Preserving Fairness Guarantees in Changing Environmentscited by 2
  12. 2021A generative, predictive model for menstrual cycle lengths that accounts for potential self-tracking artifacts in mobile health datacited by 1
11 more
  1. 2021A Review on Oracle Issues in Machine Learningcited by 1
  2. 2022Some performance considerations when using multi-armed bandit algorithms in the presence of missing datacited by 1
  3. 2023HypUC: Hyperfine Uncertainty Calibration with Gradient-boosted Corrections for Reliable Regression on Imbalanced Electrocardiogramscited by 1
  4. 2025Central Limit Theorems for Transition Probabilities of Controlled Markov Chainscited by 1
  5. 2023PDRL: Multi-Agent based Reinforcement Learning for Predictive Monitoringcited by 0
  6. 2023Nonparametric modeling of the composite effect of multiple nutrients on blood glucose dynamicscited by 0
  7. 2024Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosiscited by 0
  8. 2025Time After Time: Deep-Q Effect Estimation for Interventions on When and What to docited by 0
  9. 2025DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detectioncited by 0
  10. 2025Prediction of Hospital Associated Infections During Continuous Hospital Stayscited by 0
  11. 2026Neuron Incidence Redistribution for Fairness in Medical Image Classificationcited by 0