In Health policy (Amsterdam, Netherlands)
OBJECTIVE : The purpose of this study was to use a deep learning model and a traditional statistical regression model to predict the long-term care insurance decisions of registered nurses.
METHODS : We Prospectively surveyed 1,373 registered nurses with a minimum of 2 years of full-time working experience at a large medical center in Taiwan: 615 who already owned long-term care insurance (LTCI), 332 who had no intention to purchase LTCI (group 1), and 426 who intended to purchase LTCI (group 2).
RESULTS : After inverse probability of treatment weighting (IPTW), no statistically significant differences were identified in the study characteristics of the two groups. All the performance indices for the deep neural network (DNN) model were significantly higher than those of the multiple logistic regression (MLR) model (P<0.001). The strongest predictor of an individual's long-term care insurance decision was their risk propensity score, followed by their caregiving responsibilities, whether they live with older adult relatives, their experiences of catastrophic illness, and their openness to experience.
CONCLUSIONS : The DNN model is useful for predicting long-term care insurance decisions. Its prediction accuracy can be increased through training with temporal data collected from registered nurses. Future research can explore designs for two-level or multilevel models that explain the contextual effects of the risk factors on long-term care insurance decisions.
Shi Hon-Yi, Yeh Shu-Chuan Jennifer, Chou Hsueh-Chih, Wang Wen Chun
2023-Jan-18
Deep neural networks, Feature importance analysis, Inverse probability of treatment weighting, Long-term care insurance, Multiple logistic regression
