ArticleBMC geriatrics2022
Latent trajectories of frailty and risk prediction models among geriatric community dwellers: an interpretable machine learning perspective.
Article in BMC geriatrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction of frailty in community older adults based on machine learning: a systematic review and meta-analysis.Frontiers in public health · 2025Pooled it
- An explainable online frailty prediction model for community-dwelling older adults based on machine learning algorithms: a cross-sectional study based on retrospective health data.Annals of medicine · 2026Article
- Comparative Analysis by Machine Learning of Geriatric Frailty and Alzheimer's Disease Classification Using Independent Datasets.Life (Basel, Switzerland) · 2026Article
- Machine Learning-Based Frailty Prediction and Classification in Community-Dwelling Older Adults: A Systematic Review of Validation, Explainability, and Implementation Readiness.Healthcare (Basel, Switzerland) · 2026Review
- ProtFI, an efficient frailty-trained proteomics-based biomarker of aging, robustly predicts age-related decline.Cell reports methods · 2026Article
- Frailty trajectories and mortality prediction among Japanese community-dwelling older adults: a cohort study comparing time-based and age-based models.Archives of public health = Archives belges de sante publique · 2026Article
- Explainable Machine Learning Approaches Predict Frailty and Adverse Outcomes in Older Adults: Development and Validation with Two Longitudinal Cohorts.Journal of clinical medicine · 2026Article
- Frailty Trajectories and Their Predictors in Chinese Empty-Nest Older Adults: An 8-Year Longitudinal Study.Healthcare (Basel, Switzerland) · 2026Article
- A Machine Learning Approach for Predicting Long-term Care Needs and Identifying Risk Factors Among Older Adults in Japan.The Kobe journal of medical sciences · 2025Article
- The incremental predictive value of biological aging indicators for cognitive impairment in older adults: a longitudinal analysis on the Mr. OS & Ms. OS cohort.Alzheimer's research & therapy · 2025Article
- Identification of Frailty Clusters Using Cross-Sectional Frailty and Frailty Trajectory: Cohort of Heart Failure Veterans.JACC. Advances · 2025Article
- Explainable machine learning framework for biomarker discovery by combining biological age and frailty prediction.Scientific reports · 2025Article
- Development and validation of PRE-FRA (PREdiction of FRAilty risk in community older adults) frailty prediction model.Frontiers in public health · 2025Article
- Development and validation of nomograms to predict frailty-worsening trajectories among Chinese older adults.Frontiers in public health · 2025Article
- Frailty prediction in patients with chronic digestive system diseases: based on multi-task learning model.Frontiers in medicine · 2025Article
- Determinants and risk prediction models for frailty among community-living older adults in eastern China.Frontiers in public health · 2025Article
- Explainable machine learning model for pre-frailty risk assessment in community-dwelling older adults.Health care science · 2024Article
- Effect of leisure activity on frailty trajectories among Chinese older adults: a 16-year longitudinal study.BMC geriatrics · 2024Article
- A Mendelian randomization study to examine the causal associations of circulating micronutrient levels with frailty risk.Frontiers in nutrition · 2024Article
- What If the Clinical and Older Adults' Perspectives about Frailty Converge? A Call for a Mixed Conceptual Model of Frailty: A Traditional Literature Review.Healthcare (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to identify long-term frailty trajectories among older adults (≥65) and construct interpretable prediction models to assess the risk of developing abnormal frailty trajectory among older adults and examine significant factors related to the progression of frailty.
methodsThis study retrospectively collected data from the Chinese Longitudinal Healthy Longevity and Happy Family Study between 2002 and 2018 (N = 4083). Frailty was defined by the frailty index. The whole study consisted of two phases of tasks. First, group-based trajectory modeling was used to identify frailty trajectories. Second, easy-to-access epidemiological data was utilized to construct machine learning algorithms including naïve bayes, logistic regression, decision tree, support vector machine, random forest, artificial neural network, and extreme gradient boosting to predict the risk of long-term frailty trajectories. Further, Shapley additive explanations was employed to identify feature importance and open-up the black box model of machine learning to further strengthen decision makers' trust in the model.
resultsTwo distinct frailty trajectories (stable-growth: 82.54%, rapid-growth: 17.46%) were identified. Compared with other algorithms, random forest performed relatively better in distinguishing the stable-growth and rapid-growth groups. Physical function including activities of daily living and instrumental activities of daily living, marital status, weight, and cognitive function were top five predictors.
conclusionsInterpretable machine learning can achieve the primary goal of risk stratification and make it more transparent in individual prediction beneficial to primary screening and tailored prevention.
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.