Predictive model for *** mortality after breast cancer surgery in Taiwan residents

作     者：Su-Hsin Huang Joon-Khim Loh Jinn-Tsong Tsai Ming-Feng Houg Hon-Yi Shi 

作者机构：Department of Nursing Kaohsiung Municipal Hsiao-Kang Hospital Kaohsiung Medical University Kaohsiung Taiwan China Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University 100.Shih.Chun 1st Road Kaohsiung Taiwan China Divison of Neurosurgery Department of Surgery Kaohsiung Medical University Hospital Kaohsiung Taiwan China Department of Surgery Kaohsiung Municipal Ta-Tung Hospital Kaohsiung Taiwan China. Department of Computer Science National Pingtung University Pingtung Taiwan China Division of General & Gastroenterological Surgery Department of Surgery Kaohsiung Municipal Hsiao-Kang Hospital Kaohsiung Taiwan China Cancer Center Kaohsiung Medical University Hospital and Institute of Clinical Medicine Kaohsiung Medical University Kaohsiung Taiwan China Department of Business Management National Sun Yat-sen University Kaohsiung Taiwan China 

出 版 物：《Chinese Journal of Cancer》 (Chin. J. Cancer)

年 卷 期：2017年第36卷第4期

页      面：184-192页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：supported by funding from“the Ministry of Science and Technology”in Taiwan China(MOST 102-2314-B-037-043) 

主　　题：Breast cancer surgery Artificial neural networks Multiple logistic regression Cox regression 5-year mortality 

摘      要：Background:Few studies of breast cancer surgery outcomes have used longitudinal data for more than 2 *** study aimed to validate the use of the artificial neural network(ANN)model to predict the 5?year mortality of breast cancer patients after surgery and compare predictive accuracy between the ANN model,multiple logistic regression(MLR)model,and Cox regression ***:This study compared the MLR,Cox,and ANN models based on clinical data of 3632 breast cancer patients who underwent surgery between 1996 and *** estimation dataset was used to train the model,and a validation dataset was used to evaluate model *** sensitivity analysis was also used to assess the relative signifi?cance of input variables in the prediction ***:The ANN model significantly outperformed the MLR and Cox models in predicting 5?year mortality,with higher overall performance *** results indicated that the 5?year postoperative mortality of breast cancer patients was significantly associated with age,Charlson comorbidity index(CCI),chemotherapy,radiotherapy,hormone therapy,and breast cancer surgery volumes of hospital and surgeon(all P0.05).Breast cancer surgery volume of surgeon was the most influential(sensitive)variable affecting 5?year mortality,followed by breast cancer surgery volume of hospital,age,and ***:Compared with the conventional MLR and Cox models,the ANN model was more accurate in predict?ing 5?year mortality of breast cancer patients who underwent *** mortality predictors identified in this study can also be used to educate candidates for breast cancer surgery with respect to the course of recovery and health outcomes.