ReviewJournal of internal medicine2022
Predictive models for clinical decision making: Deep dives in practical machine learning.
Review in Journal of internal medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 1 of them a synthesis 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
69 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of clinical prediction models for identifying postmenopausal osteoporosis: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2026Pooled it
- Who engages? Machine learning insights into digital mindfulness-based intervention for generalized anxiety disorder.Journal of affective disorders · 2026Trial
- Trial
- Machine learning-based risk factors for acute-on-chronic liver failure in alcohol-associated liver disease.Annals of medicine · 2026Observational
- An explainable hybrid SMPR-Net-XGBoost framework for automated lupus nephritis detection using medical image analysis.International urology and nephrology · 2026Article
- Explainable artificial intelligence in ICU admission prediction for methanol poisoning patients: a comparative study of deep and machine learning models.International journal of emergency medicine · 2026Article
- Prevalence, risk-factors and early prediction of impaired kidney function among indigenous adults in Odisha, India: A secondary analysis.The Indian journal of medical research · 2026Article
- Current adoption of the Prosthodontic Diagnostic Index for clinical care-A cross-sectional survey study.Journal of prosthodontics : official journal of the American College of Prosthodontists · 2026Article
- Development and validation of machine-learning diagnostic models for identifying frailty in older adults with tuberculosis: a multicentre observational study protocol.BMC pulmonary medicine · 2026Observational
- Longitudinal Trends in Noncommunicable Disease Risk Factors and Premature Mortality in Saudi Arabia: A 33-Year Ecological Time-Series Study with Machine Learning Prediction.Journal of clinical medicine · 2026Article
- A nomogram with online dynamic calculator for predicting osteoporosis: development and validation based on NHANES.BMC musculoskeletal disorders · 2026Article
- Integrating large language models for enhanced predictive analytics in healthcare.NPJ digital medicine · 2026Article
- Development and Internal Validation of a Gradient Boosting Model for Pressure Injury Risk in the ICU.International wound journal · 2026Article
- From Disease-Specific Models to Broad Clinical Utility: A Perspective on AI Hybrid Ensemble Frameworks.Advanced genetics (Hoboken, N.J.) · 2026Article
- Understanding and applying machine learning in differentiating abdominal IgA vasculitis without purpura from appendicitis.Pediatric research · 2026Article
- Unveiling the Gaps: Machine Learning Models for Unmeasured Ions.Diagnostics (Basel, Switzerland) · 2026Article
- Machine learning models for drug-drug interaction prediction from computational discovery to clinical application.NPJ digital medicine · 2026Review
- Improving Clinical Diagnostics and Patient Care through Artificial Intelligence and Biosensor Technologies.ACS omega · 2026Review
- Towards accurate artificial intelligence models for strain-level phage-host prediction.Briefings in bioinformatics · 2026Article
- Artificial Intelligence Tools in Myocardial Infarction Prognosis: Evaluating the Performance of Machine Learning and Deep Learning Models.Current cardiology reviews · 2026Review
9 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The deployment of machine learning for tasks relevant to complementing standard of care and advancing tools for precision health has gained much attention in the clinical community, thus meriting further investigations into its broader use. In an introduction to predictive modelling using machine learning, we conducted a review of the recent literature that explains standard taxonomies, terminology and central concepts to a broad clinical readership. Articles aimed at readers with little or no prior experience of commonly used methods or typical workflows were summarised and key references are highlighted. Continual interdisciplinary developments in data science, biostatistics and epidemiology also motivated us to further discuss emerging topics in predictive and data-driven (hypothesis-less) analytics with machine learning. Through two methodological deep dives using examples from precision psychiatry and outcome prediction after lymphoma, we highlight how the use of, for example, natural language processing can outperform established clinical risk scores and aid dynamic prediction and adaptive care strategies. Such realistic and detailed examples allow for critical analysis of the importance of new technological advances in artificial intelligence for clinical decision-making. New clinical decision support systems can assist in prevention and care by leveraging precision medicine.
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.