ArticleThe journals of gerontology. Series A, Biological sciences and medical sciences2021
Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults.
Article in The journals of gerontology. Series A, Biological sciences and medical sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Systematic Review of Multivariate Models for Predicting Fall-Related Injuries in Older Adults.Journal of nursing management · 2026Pooled it
- A systematic review of chiropractic care for fall prevention: rationale, state of the evidence, and recommendations for future research.BMC musculoskeletal disorders · 2022Pooled it
- Observational
- The interplay of risk factors for future falls and fall-related injuries among community-dwelling older adults: a Bayesian network analysis.BMC public health · 2026Article
- Statistical Methods in Aging Research: Improving Current Practices and Embracing Emerging Approaches.Annual review of statistics and its application · 2026Article
- Predicting fall risk among older adults with sarcopenia in China using machine learning models: a six-year longitudinal study from CHARLS.BMC geriatrics · 2026Article
- Machine Learning Models for Predicting Antipsychotic Effectiveness and Separate Cost-Effectiveness Analysis in Hospitalized Schizophrenia Patients.Neuropsychiatric disease and treatment · 2026Article
- Article
- A machine learning-based fall-risk score for severity of fall-related adverse outcomes in community older adults.BMC geriatrics · 2025Article
- Methodological Review of Classification Trees for Risk Stratification: An Application Example in the Obesity Paradox.Nutrients · 2025Review
- Urban-rural disparities in fall risk among older Chinese adults: insights from machine learning-based predictive models.Frontiers in public health · 2025Article
- Gerontologic Biostatistics and Data Science: Aging Research in the Era of Big Data.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024Review
- Impact of sleep duration and dietary patterns on risk of metabolic syndrome in middle-aged and elderly adults: a cross-sectional study from a survey in Anhui, Eastern China.Lipids in health and disease · 2024Article
- [Development of a fall prediction model for community-dwelling older adults in South Korea using machine learning: a secondary data analysis].Journal of Korean biological nursing science · 2024Article
- Article
- Ensemble Algorithm for Risk Prediction of Clinical Failure After Anterior Cruciate Ligament Reconstruction.Orthopaedic journal of sports medicine · 2024Article
- Predicting factors for extremity fracture among border-fall patients using machine learning computing.Heliyon · 2024Article
- Unveiling Fall Triggers in Older Adults: A Machine Learning Graphical Model Analysis.Mathematics (Basel, Switzerland) · 2024Article
- Comparative study of machine learning methods for modeling associations between risk factors and future dementia cases.GeroScience · 2024Article
- Interpretable machine learning model for early prediction of delirium in elderly patients following intensive care unit admission: a derivation and validation study.Frontiers in medicine · 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
10 authors.
Funding
Abstract
backgroundAdvances in computational algorithms and the availability of large datasets with clinically relevant characteristics provide an opportunity to develop machine learning prediction models to aid in diagnosis, prognosis, and treatment of older adults. Some studies have employed machine learning methods for prediction modeling, but skepticism of these methods remains due to lack of reproducibility and difficulty in understanding the complex algorithms that underlie models. We aim to provide an overview of two common machine learning methods: decision tree and random forest. We focus on these methods because they provide a high degree of interpretability.
methodWe discuss the underlying algorithms of decision tree and random forest methods and present a tutorial for developing prediction models for serious fall injury using data from the Lifestyle Interventions and Independence for Elders (LIFE) study.
resultsDecision tree is a machine learning method that produces a model resembling a flow chart. Random forest consists of a collection of many decision trees whose results are aggregated. In the tutorial example, we discuss evaluation metrics and interpretation for these models. Illustrated using data from the LIFE study, prediction models for serious fall injury were moderate at best (area under the receiver operating curve of 0.54 for decision tree and 0.66 for random forest).
conclusionsMachine learning methods offer an alternative to traditional approaches for modeling outcomes in aging, but their use should be justified and output should be carefully described. Models should be assessed by clinical experts to ensure compatibility with clinical practice.
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.