Demand prediction of medical services in home and community-based services for older adults in China using machine learning

Datos Bibliográficos

ID22069780
AutoresYucheng Huang (0000-0002-7818-8811, Wenzhou Medical University), Tingke Xu (Wenzhou Medical University), Qingren Yang (0000-0002-2539-665X, Wenzhou Medical University), Chengxi Pan (0009-0004-5401-5555, Xiamen University), Zhan Lü (0000-0001-8518-2090, Wenzhou Medical University), Lu Zhan, Huajian Chen (0000-0002-4358-6289, Wenzhou Medical University), Xiangyang Zhang (0000-0003-3326-382X, Wenzhou Medical University, autor de correspondencia), Chun Chen (0000-0002-3608-6972, Wenzhou Medical University, autor de correspondencia)
Año2023
Volumen11
Páginas1142794-1142794
Fecha de publicación2023-03-16
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaFrontiers in Public Health (JOURNAL)
Identificadores de la revistaISSN: 2296-2565 • E-ISSN: 2296-2565
EditorialFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2023.1142794
PMID37006569
OpenAlexW4327614598
IdiomaEN
Citas recibidas2
Referencias citadas65

Background: Home and community-based services are considered an appropriate and crucial caring method for older adults in China. However, the research examining demand for medical services in HCBS through machine learning techniques and national representative data has not yet been carried out. This study aimed to address the absence of a complete and unified demand assessment system for home and community-based services. Methods: This was a cross-sectional study conducted on 15,312 older adults based on the Chinese Longitudinal Healthy Longevity Survey 2018. Models predicting demand were constructed using five machine-learning methods: Logistic regression, Logistic regression with LASSO regularization, Support Vector Machine, Random Forest, and Extreme Gradient Boosting (XGboost), and based on Andersen's behavioral model of health services use. Methods utilized 60% of older adults to develop the model, 20% of the samples to examine the performance of models, and the remaining 20% of cases to evaluate the robustness of the models. To investigate demand for medical services in HCBS, individual characteristics such as predisposing, enabling, need, and behavior factors constituted four combinations to determine the best model. Results: Random Forest and XGboost models produced the best results, in which both models were over 80% at specificity and produced robust results in the validation set. Andersen's behavioral model allowed for combining odds ratio and estimating the contribution of each variable of Random Forest and XGboost models. The three most critical features that affected older adults required medical services in HCBS were self-rated health, exercise, and education. Conclusion: Andersen's behavioral model combined with machine learning techniques successfully constructed a model with reasonable predictors to predict older adults who may have a higher demand for medical services in HCBS. Furthermore, the model captured their critical characteristics. This method predicting demands could be valuable for the community and managers in arranging limited primary medical resources to promote healthy aging

Gradient boosting · Logistic regression · Machine learning · Odds · Predictive modelling · Random forest · Support vector machine · Computer Science · Global Health Care Issues · Healthcare Systems and Reforms · Intergenerational Family Dynamics and Caregiving · Medicine · Artificial Intelligence · Gerontology