| Summary: | 碩士 === 高雄醫學大學 === 醫務管理暨醫療資訊學系碩士在職專班 === 102 === Three purposes of this study:
1. To evaluate the changing trends of patient characteristics and hospital characteristics during the study period;
2. To compare the performance indices between artificial neural networks (ANN), logistic regression (LR) and Cox proportional hazards (COX) models;
3. To conduct the global sensitivity analysis in order to weight these significant predictors.
Research Methods
This nationwide population-base study retrospectively conducted the claims data from 1996 to 2010. Included criteria were the patients after psychiatric disorders with age larger than 18 years old; the patients with ICD-9-CM diagnosis codes of 290~319, and use patient characteristics and hospital characteristics to identify the impact factors of 5-year mortality after diagnosis of psychiatric disorders.The comparison of performance indices of ANN, LR, and COX models were employed to predict the 5-yar mortality rate. The global sensitivity analysis was also used to weight these significant predictors.
Results
The results showed that ANN model is better than LR and COX models in predicting these performance indices: sensitivity (40.82％ vs. 31.50％ vs. 36.37％), NPV (74.23％ vs. 70.90％ vs. 11.48％), accuracy (71.35％ vs. 68.82％ vs. 40.53％), area under the accept operating characteristic curve (AUROC) (85％ vs. 72％ vs. 65％). Overall for the 5-year mortality rate, the ANN model also showed the better performance indices than the LR and COX models.
Conclusions and Suggestions
The ANN model showed the better performance indices than the LR and COX models. Medical research can therefore use this model as a multi-clinical assessment and decision making, and to identify the most important predictors to improve quality of care. Psychiatric specialist hospital is major medical provider in Taiwan. It is suggested that both physical and mental examinations should be strengthened in order to achieve early detection and early treatment in order to reduce 5-year mortality and consumption of medical resources utilization.
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