ArticleFrontiers in public health2025
Development of a visualized risk prediction system for sarcopenia in older adults using machine learning: a cohort study based on CHARLS.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Early Prediction of 90-Day Periprosthetic Joint Infection After Hip Arthroplasty for Proximal Femur Fracture Using Machine Learning: Development and Temporal Validation of a Predictive Model.Journal of clinical medicine · 2026Article
- Supervised Machine Learning-Based Prediction of In-Hospital Mortality Following Hip Fracture in Older Adults.Diagnostics (Basel, Switzerland) · 2026Article
- Exploration of an interpretable machine learning-based screening manner for low muscle mass among Chinese community-dwelling older adults using routine physical examination information.BMC geriatrics · 2026Article
- "Short" is not always "scientific": cross-sectional quality assessment and machine learning-based evaluation of weight management short videos on TikTok and Bilibili.Frontiers in public health · 2026Article
- Primary healthcare professionals' perspectives on mobility limitation assessment and predictive model optimization for community-dwelling older adults: a qualitative research.Frontiers in public health · 2026Article
- Development and validation of a machine learning-based risk prediction model for sarcopenia in community hospital patients: a retrospective cohort study.Frontiers in aging · 2026Article
- A study on promoting AI learning and usage behaviors among health management students from the perspective of the "knowledge-belief-action" model.Frontiers in public health · 2026Article
- Modeling older adults' continuance intention toward mobile health apps: a dual-path SEM-ANN approach.BMC public health · 2025Article
- METS-IR predicts new-onset stroke in older adults with stages 0-3 cardiovascular-kidney-metabolic syndrome: a prospective, multicohort, clinical study with statistical and machine learning.Diabetology & metabolic syndrome · 2025Article
- Predictive Model for Managing the Clinical Risk of Emergency Department Patients: A Systematic Review.Journal of clinical medicine · 2025Review
- Anthropometric Measurements for Predicting Low Appendicular Lean Mass Index for the Diagnosis of Sarcopenia: A Machine Learning Model.Journal of functional morphology and kinesiology · 2025Article
- Development and validation of a non-invasive prediction model for identifying high-risk children with metabolic dysfunction-associated fatty liver disease.Frontiers in pediatrics · 2025Article
- Predictive features analysis and nomogram construction for predicting depression in elderly patients.Frontiers in psychology · 2025Article
- Identification of sepsis biomarkers through glutamine metabolism-mediated immune regulation: a comprehensive analysis employing mendelian randomization, multi-omics integration, and machine learning.Frontiers in immunology · 2025Article
- Construct prediction models for low muscle mass with metabolic syndrome using machine learning.PloS one · 2025Article
- Predicting the risk of metabolic-associated fatty liver disease in the elderly population in China: construction and evaluation of interpretable machine learning models.Frontiers in medicine · 2025Article
- A risk prediction system for depression in middle-aged and older adults grounded in machine learning and visualization technology: a cohort study.Frontiers in public health · 2025Article
- Construction and validation of nomogram prediction model for anxiety and depression in chemotherapy patients with multiple myeloma.Frontiers in psychiatry · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: The older adult are at high risk of sarcopenia, making early identification and scientific intervention crucial for healthy aging. Methods: This study utilized data from the China Health and Retirement Longitudinal Study (CHARLS), including a cohort of 2,717 middle-aged and older adult participants. Ten machine learning algorithms, such as CatBoost, XGBoost, and NGBoost, were used to construct predictive models. Results: Among these algorithms, the XGBoost model performed the best, with an ROC-AUC of 0.7, and was selected as the final predictive model for sarcopenia risk. SHAP technology was used to visualize the prediction results, enhancing the interpretability of the model, and the system was built on a web platform. Discussion: The system provides the probability of sarcopenia onset within 4 years based on input variables and identifies critical influencing factors. This facilitates understanding and use by medical professionals. The system supports early identification and scientific intervention for sarcopenia in the older adult, offering significant clinical value and application potential.
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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 Socratic graph.