ArticleJournal of applied gerontology : the official journal of the Southern Gerontological Society2023
Predicting Hospitalization among Medicaid Home- and Community-Based Services Users Using Machine Learning Methods.
Article in Journal of applied gerontology : the official journal of the Southern Gerontological Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Promising algorithms to perilous applications: a systematic review of risk stratification tools for predicting healthcare utilisation.BMJ health & care informatics · 2024Pooled it
- No More False Alert: Contrastive Learning for Predicting Health Deterioration from Imbalanced Care Records.Sensors (Basel, Switzerland) · 2026Article
- Discharge disposition for home health care patients with Alzheimer's disease and related dementia: The role of living arrangements and rural living.The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We compare multiple machine learning algorithms and develop models to predict future hospitalization among Home- and Community-Based Services (HCBS) Users. Furthermore, we calculate feature importance, the score of input variables based on their importance to predict the outcome, to identify the most relevant variables to predict hospitalization. We use the 2012 national Medicaid Analytic eXtract data and Medicare Provider Analysis and Review data. Predicting any hospitalization, Random Forest appears to be the most robust approach, though XGBoost achieved similar predictive performance. While the importance of features varies by algorithm, chronic conditions, previous hospitalizations, as well as use of services for ambulance, personal care, and durable medical equipment were generally found to be important predictors of hospitalization. Utilizing prediction models to identify those who are prone to hospitalization could be useful in developing early interventions to improve outcomes among HCBS users.
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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.