ArticleAge and ageing2024
New horizons in prediction modelling using machine learning in older people's healthcare research.
Article in Age and ageing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
What it found
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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
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evaluating methodological quality of prognostic prediction models on patient-reported outcome measurements after total hip and total knee arthroplasty : a systematic review.Bone & joint open · 2026Pooled it
- Prediction models for the occurrence and mortality of sepsis-associated lung injury: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- The Effectiveness of Machine Learning Algorithms in Predicting Healthcare Service Quality Metrics: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Development and Validation of an Interpretable Machine Learning Model for Predicting ICU-Acquired Weakness in Postoperative Patients.Nursing in critical care · 2026Article
- Recalibrating Resting Energy Expenditure Prediction Equations in Asian Older Adults with Multimorbidity.Nutrients · 2026Article
- Machine Learning-Based Prediction of Institutional Delivery Dropout (IDD) Among Nigerian Women: An Exploratory Study Using SHAP Interpretability.Journal of epidemiology and global health · 2026Article
- Social disadvantage outweighs knowledge in dementia risk.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Sleep duration, sleep quality, and daytime napping in relation to incident cardiometabolic multimorbidity across three national aging cohorts: a prospective, interpretable machine learning study.Frontiers in nutrition · 2026Article
- Machine learning algorithms for predicting glycemic control and weight loss outcomes in GLP-1 receptor agonist users.Frontiers in artificial intelligence · 2026Article
- Development and internal validation of an interpretable machine learning model using first-24-h postpartum nursing variables to predict delayed lactogenesis II after cesarean delivery.Frontiers in medicine · 2026Article
- Development and validation of a prediction model for infection in chronic nonhealing wounds: a two-center retrospective study with external validation.Frontiers in public health · 2026Article
- Identification of BMI-related high-risk feature combinations for diabetes among young adults with normal baseline fasting plasma glucose using interpretable machine learning: a health check-up cohort study.Frontiers in endocrinology · 2026Article
- Explainable machine learning for predicting clinical outcomes in HIV/TB co-infection: a comparative retrospective study.BMC infectious diseases · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Machine learning (ML) and prediction modelling have become increasingly influential in healthcare, providing critical insights and supporting clinical decisions, particularly in the age of big data. This paper serves as an introductory guide for health researchers and readers interested in prediction modelling and explores how these technologies support clinical decisions, particularly with big data, and covers all aspects of the development, assessment and reporting of a model using ML. The paper starts with the importance of prediction modelling for precision medicine. It outlines different types of prediction and machine learning approaches, including supervised, unsupervised and semi-supervised learning, and provides an overview of popular algorithms for various outcomes and settings. It also introduces key theoretical ML concepts. The importance of data quality, preprocessing and unbiased model performance evaluation is highlighted. Concepts of apparent, internal and external validation will be introduced along with metrics for discrimination and calibration for different types of outcomes. Additionally, the paper addresses model interpretation, fairness and implementation in clinical practice. Finally, the paper provides recommendations for reporting and identifies common pitfalls in prediction modelling and machine learning. The aim of the paper is to help readers understand and critically evaluate research papers that present ML models and to serve as a first guide for developing, assessing and implementing their own.
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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.