ReviewAnnual review of biomedical data science2021
Probabilistic Machine Learning for Healthcare.
Review in Annual review of biomedical data science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Predicting Dental Anxiety and Cooperative Behavior in Children Using Machine Learning: A Cross-Sectional Predictive Modeling Study.Dentistry journal · 2026Article
- Immune system up-regulation in securely attached infants during early childhood.Brain, behavior, & immunity - health · 2026Article
- Multicenter validation of a machine learning model for predicting intrapartum high fever in parturients receiving labor analgesia.Frontiers in medicine · 2026Article
- Deep hierarchical subtyping of multi-organ systemic sclerosis trajectories - a EUSTAR study.NPJ digital medicine · 2025Article
- AI-driven multi-agent reinforcement learning framework for real-time monitoring of physiological signals in stress and depression contexts.Brain informatics · 2025Article
- A novel predictive method for URS and laser lithotripsy using machine learning and explainable AI: results from the FLEXOR international database.World journal of urology · 2025Article
- AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Individualized post-crisis monitoring of psychiatric patients via Hidden Markov models.Frontiers in digital health · 2024Article
- Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities.AIMS public health · 2024Article
- Scalable and interpretable alternative to chart review for phenotype evaluation using standardized structured data from electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Diabetic Foot Ulcer Identification: A Review.Diagnostics (Basel, Switzerland) · 2023Review
- Interpretable clinical phenotypes among patients hospitalized with COVID-19 using cluster analysis.Frontiers in digital health · 2023Article
- Quantifying deep neural network uncertainty for atrial fibrillation detection with limited labels.Scientific reports · 2022Article
- Some performance considerations when using multi-armed bandit algorithms in the presence of missing data.PloS one · 2022Article
- A Generative Modeling Approach to Calibrated Predictions: A Use Case on Menstrual Cycle Length Prediction.Proceedings of machine learning research · 2021Article
- Machine learning in patient flow: a review.Progress in biomedical engineering (Bristol, England) · 2021Review
- Incorporating External Information in Tissue Subtyping: A Topic Modeling Approach.Proceedings of machine learning research · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.