ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
A Review of Challenges and Opportunities in Machine Learning for Health.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05311046 (Biomarker-enhanced Artificial Intelligence Based Pediatric Sepsis Screening Tool Towards Early Recognition and Personalized Therapeutics), which is not on this map. Cited by 177 papers, 5 of them syntheses that pooled it.
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.
Biomarker-enhanced Artificial Intelligence Based Pediatric Sepsis Screening Tool Towards Early Recognition and Personalized Therapeutics
Who cites it
177 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Large Language Models in Colorectal Cancer Care and Clinical Decision Support: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Artificial Intelligence and Machine Learning-based prediction of tuberculosis treatment failure: A systematic review and meta-analysis.PloS one · 2026Pooled it
- Machine learning in paediatric haematological malignancies: a systematic review of prognosis, toxicity and treatment response models.Pediatric research · 2025Pooled it
- Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis.Frontiers in digital health · 2025Pooled it
- Pooled it
- MicroRNome analysis generates a blood-based signature for endometriosis.Scientific reports · 2022Trial
- Beyond Biomarkers: Reclaiming the Central Role of Clinical Neurophysiology in Neuromuscular Disorders.Brain sciences · 2026Review
- Cell-based therapies of autoimmune diseases in the context of artificial intelligence development.Clinical and experimental medicine · 2026Review
- Review
- Article
- Functional disability screening in the elderly: a machine learning approach with ELSI-Brazil data.GeroScience · 2026Article
- Bias in AI in Epic Electronic Medical Records and Its Impact on Executive Communication: Conceptual Analysis.Online journal of public health informatics · 2026Article
- Artificial Intelligence Empowers Physical Therapy for Older Adults: A Perspective.Health science reports · 2026Article
- PHO-Agents: A Large Language Model-Powered Multi-Agent System for Predicting Health Outcomes.medRxiv : the preprint server for health sciences · 2026Article
- Research through Evaluation for Large Language Model in Patient-Clinician Communications.Research square · 2026Article
- Interpretable prediction of neonatal mortality and its key predictors using machine learning and SHAP analysis.BMC medical informatics and decision making · 2026Article
- Composite Certainty: Addressing Metric Degeneracy in Parameter Inference for Model-Based Diagnostics.bioRxiv : the preprint server for biology · 2026Article
- Hybrid Modelling of Pulmonary Cancer Risk Prediction Using Classical Algorithms to Modern Machine Learning Techniques.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- AI/ML-based prediction of TB treatment failure: A systematic review and meta-analysis.medRxiv : the preprint server for health sciences · 2026Article
- An explainable imaging-clinical biomarker for non-small cell lung cancer prognostication based on normalised hotspot to centroid distance and [European journal of nuclear medicine and molecular imaging · 2026Article
117 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Modern electronic health records (EHRs) provide data to answer clinically meaningful questions. The growing data in EHRs makes healthcare ripe for the use of machine learning. However, learning in a clinical setting presents unique challenges that complicate the use of common machine learning methodologies. For example, diseases in EHRs are poorly labeled, conditions can encompass multiple underlying endotypes, and healthy individuals are underrepresented. This article serves as a primer to illuminate these challenges and highlights opportunities for members of the machine learning community to contribute to healthcare.
Identifiers
32477638PMC7233077What 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.