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標題Title: Comparisons of Prediction Models of Quality of Life after Laparoscopic Cholecystectomy: A Longitudinal Prospective Study
作者Authors: 趙頌慈
上傳單位Department: 電機工程系
上傳時間Date: 2015-12-31
上傳者Author: 趙頌慈
審核單位Department: 電機工程系
審核老師Teacher: 趙頌慈
檔案類型Categories: 論文Thesis
關鍵詞Keyword: Prediction Models of Quality of Life, Laparoscopic Cholecystectomy
摘要Abstract: Background: Few studies of laparoscopic cholecystectomy (LC) outcome have used longitudinal data for more than two
years. Moreover, no studies have considered group differences in factors other than outcome such as age and nonsurgical
treatment. Additionally, almost all published articles agree that the essential issue of the internal validity (reproducibility) of
the artificial neural network (ANN), support vector machine (SVM), Gaussian process regression (GPR) and multiple linear
regression (MLR) models has not been adequately addressed. This study proposed to validate the use of these models for
predicting quality of life (QOL) after LC and to compare the predictive capability of ANNs with that of SVM, GPR and MLR.
Methodology/Principal Findings: A total of 400 LC patients completed the SF-36 and the Gastrointestinal Quality of Life
Index at baseline and at 2 years postoperatively. The criteria for evaluating the accuracy of the system models were mean
square error (MSE) and mean absolute percentage error (MAPE). A global sensitivity analysis was also performed to assess
the relative significance of input parameters in the system model and to rank the variables in order of importance.
Compared to SVM, GPR and MLR models, the ANN model generally had smaller MSE and MAPE values in the training data
set and test data set. Most ANN models had MAPE values ranging from 4.20% to 8.60%, and most had high prediction
accuracy. The global sensitivity analysis also showed that preoperative functional status was the best parameter for
predicting QOL after LC.
Conclusions/Significance: Compared with SVM, GPR and MLR models, the ANN model in this study was more accurate in
predicting patient-reported QOL and had higher overall performance indices. Further studies of this model may consider
the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed
outcome data.

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