A study of Long Stay and Readmission on Stroke Patient

• Main Authors: Chun-Kai Liu, 劉純凱
• Other Authors: 張俊郎
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/652226

碩士 === 國立虎尾科技大學 === 工業工程與管理研究所 === 102 === Stroke is a common domestic disease. According to the statistics of Ministry of Health and Welfare, Executive Yuan (MHW), cerebrovascular disease ranks third among the ten leading causes of death for 2012. According to the Post-acute Care(PAC) Model Promotion Plan of MHW, extended hospital stay has been seen in stroke patients, about 10.4% had extended hospitalization, accounting for 38.9% of the total number of hospitalization days for stroke patient cases and 47.8% of the total hospitalization costs. It was pointed out in the plan that community hospitals with a professional team to provide acute post-care significantly contributed to patients’ functional recovery and reduced number of hospitalization days. Hence, acute post-care is said to be significantly effective. How to effectively assess stroke patients’ PAC is of considerable importance for hospitals, patients and their families, as well as National Health Insurance (NHI) resource use and planning. The database of stroke patients was adopted as the research participants in this study. The influential factors of the stroke patients’ extended hospitalization and re-admission were compiled. The particle swarm optimization algorithm, cross entropy algorithm, genetic logistic regression algorithm, back propagation neural network, support vector machine, and case-based reasoning system combined were used to construct 11 assessment models for predicting stroke patients’ hospitalization situations. Additionally, the case-based reasoning technique was applied to construct the stoke patients’ extended hospital stay assessment system. Findings show that in terms of the re-admission prediction model, most models had mean test accuracy exceeding 88%. In particular, the cross entropy algorithm, combined with the support vector machine model, produced the best results, with the mean accuracy reaching 88.873% and the mean area under curve of ROC (AUC) reaching 0.8349. As for the extended hospitalization assessment system, the cross entropy algorithm weights, combined with the case-based reasoning system, had the best performance, with the mean accuracy of 88.536% and the mean AUC of 0.7988. The research results shall serve as a reference for physicians and relevant medical personnel when exploring issues related to brain stroke patients’ re-admission and extended hospitalization. The results are also expected to be substantially helpful for medical institutions in medical resource planning and hospitalization quality improvement.