Back to Search
Start Over
Predicting Hospitalization among Medicaid Home- and Community-Based Services Users Using Machine Learning Methods
- Source :
- J Appl Gerontol
- Publication Year :
- 2022
- Publisher :
- SAGE Publications, 2022.
-
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.
- Subjects :
- Geriatrics and Gerontology
Gerontology
Article
Subjects
Details
- ISSN :
- 15524523 and 07334648
- Volume :
- 42
- Database :
- OpenAIRE
- Journal :
- Journal of Applied Gerontology
- Accession number :
- edsair.doi.dedup.....6aae6a40c4243cc6f1a4eb0a808363b0