ArticleJBMR plus2023
Advantages and Disadvantages of Random Forest Models for Prediction of Hip Fracture Risk Versus Mortality Risk in the Oldest Old.
Article in JBMR plus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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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.
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Who cites it
21 citing papers in PubMed, 41 citations in OpenAlex.
- Multi-Omics and Artificial Intelligence in Cardiovascular Medicine: From Mechanistic Insights to Clinical Translation.Biomedicines · 2026Review
- The Osteoporotic Fractures in Men study (MrOS): a 25-yr landmark study of skeletal health in older men.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2026Article
- Environmental drivers of soil potentially toxic metals identified through multi-scenario machine learning in an arid region of Southeastern Iran.Scientific reports · 2026Article
- Machine learning prediction of tuberculosis mortality: a comparative analysis of random survival forest and cox regression models.BMC infectious diseases · 2026Article
- Human and environmental controls on soil contamination in a dust-prone region revealed by random forest and Shapley additive explanations analysis.Scientific reports · 2026Article
- Classification of pulmonary diseases using machine learning and deep learning models on GLI-2012 standardized spirometry features.BMC medical informatics and decision making · 2026Article
- Enhanced preoperative prediction for microvascular invasion in hepatocellular carcinoma through an optimized MR Radiomics combination strategy and machine learning predictor.Frontiers in medicine · 2026Article
- Satisfaction and interpersonal process of care experience of parents with children under chiropractic care.Journal of chiropractic medicine · 2026Article
- Article
- Entropy, Assessed by Homeostatic Dysregulation on Electrocardiograms Predicts Fracture and Mortality.Aging cell · 2025Article
- From Sequence to Response: AI-Guided Prediction of Nucleic Acid Nanoparticles Immune Recognitions.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Automated feature learning and survival prognostication in grade 4 glioma using supervised machine learning models.Journal of neuro-oncology · 2025Article
- Machine Learning-Based Mobile Application for Predicting Posterior Canal Benign Paroxysmal Positional Vertigo.Laryngoscope investigative otolaryngology · 2025Article
- Risk Factors and Predictive Models for Sarcopenia in Older Adults.Aging medicine (Milton (N.S.W)) · 2025Article
- Health octo tool matches personalized health with rate of aging.Nature communications · 2025Article
- Automated Segmentation of Breast Cancer Focal Lesions on Ultrasound Images.Sensors (Basel, Switzerland) · 2025Article
- Assessment of fracture risk in diabetic patients.Journal of diabetes and metabolic disorders · 2024Review
- Prediction of childbearing tendency in women on the verge of marriage using machine learning techniques.Scientific reports · 2024Article
- Exploring putative drug properties associated with TNF-alpha inhibition and identification of potential targets in cardiovascular disease using machine learning-assisted QSAR modeling and virtual reverse pharmacology approach.Molecular diversity · 2024Article
- Anomaly Detection in Railway Sensor Data Environments: State-of-the-Art Methods and Empirical Performance Evaluation.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
Targeted fracture prevention strategies among late-life adults should balance fracture risk versus competing mortality risk. Models have previously been constructed using Fine-Gray subdistribution methods. We used a machine learning method adapted for competing risk survival time to evaluate candidate risk factors and create models for hip fractures and competing mortality among men and women aged 80 years and older using data from three prospective cohorts (Study of Osteoporotic Fractures [SOF], Osteoporotic Fracture in Men study [MrOS], Health Aging and Body Composition study [HABC]). Random forest competing risk models were used to estimate absolute 5-year risk of hip fracture and absolute 5-year risk of competing mortality (excluding post-hip fracture deaths). Models were constructed for both outcomes simultaneously; minimal depth was used to rank and select variables for smaller models. Outcome specific models were constructed; variable importance was used to rank and select variables for inclusion in smaller random forest models. Random forest models were compared to simple Fine-Gray models with six variables selected a priori. Top variables for competing risk random forests were frailty and related components in men while top variables were age, bone mineral density (BMD) (total hip, femoral neck), and frailty components in women. In both men and women, outcome specific rankings strongly favored BMD variables for hip fracture prediction while frailty and components were strongly associated with competing mortality. Model discrimination for random forest models varied from 0.65 for mortality in women to 0.81 for hip fracture in men and depended on model choice and variables included. Random models performed slightly better than simple Fine-Gray model for prediction of competing mortality, but similarly for prediction of hip fractures. Random forests can be used to estimate risk of hip fracture and competing mortality among the oldest old. Modest gains in performance for mortality without hip fracture compared to Fine-Gray models must be weighed against increased complexity. © 2023 The Authors.
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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.