SynthesisBMC medical informatics and decision making2021
Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making.
Synthesis in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning models for predicting response to epidermal growth factor receptor tyrosine kinase inhibitors in non-small cell lung cancer brain metastases: a systematic review and meta-analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Pooled it
- Comparison of machine learning methods versus traditional Cox regression for survival prediction in cancer using real-world data: a systematic literature review and meta-analysis.BMC medical research methodology · 2025Pooled it
- Toward generalizable and interpretable machine learning models in healthcare: Insights from ICU outcome predictions.Health care management science · 2026Article
- Development and External Validation of an Algorithm for Identifying HDV RNA-Positive Patients.Gastro hep advances · 2026Article
- Machine learning-based prediction of hernia risk in peritoneal dialysis patients: a comparative study of models and SHAP-driven interpretability analysis.Frontiers in medicine · 2026Article
- Real-world work productivity is impaired in people with metabolic dysfunction-associated steatotic liver disease in the USA.JHEP reports : innovation in hepatology · 2025Article
- Continual learning across population cohorts with distribution shift: insights from multi-cohort metabolic syndrome identification.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- How to use learning curves to evaluate the sample size for malaria prediction models developed using machine learning algorithms.Malaria journal · 2025Article
- Time-series deep learning and conformal prediction for improved sepsis diagnosis in primarily Non-ICU hospitalized patients.Computers in biology and medicine · 2025Article
- Benefits and harms associated with the use of AI-related algorithmic decision-making systems by healthcare professionals: a systematic review.The Lancet regional health. Europe · 2025Article
- Prediction of naloxone dose in opioids toxicity based on machine learning techniques (artificial intelligence).Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2024Article
- Primary Care Informatics: Vitalizing the Bedrock of Health Care.Journal of medical Internet research · 2024Article
- Optimizing cardiopulmonary rehabilitation duration for long COVID patients: an exercise physiology monitoring approach.GeroScience · 2024Article
- Applying machine learning approaches for predicting obesity risk using US health administrative claims database.BMJ open diabetes research & care · 2024Observational
- Identifying Predictors of PASI100 Responses up to Month 12 in Patients with Moderate-to-severe Psoriasis Receiving Biologics in the Psoriasis Study of Health Outcomes (PSoHO).Acta dermato-venereologica · 2024Observational
- Development and validation of risk prediction model for recurrent cardiovascular events among Chinese: the Personalized CARdiovascular DIsease risk Assessment for Chinese model.European heart journal. Digital health · 2024Article
- Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare.Pharmaceutics · 2024Review
- Time-domain heart rate variability features for automatic congestive heart failure prediction.ESC heart failure · 2024Article
- Closing the Digital Divide in Interventions for Substance Use Disorder.Journal of psychiatry and brain science · 2024Article
- Trustworthy and Uncertainty-Aware AI for Predicting Respiratory Complications Following Total Hip and Knee Arthroplasty.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making.
methodsThis systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist.
resultsA total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies.
conclusionsA wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.
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.