Artificial Neural Network Model for Predicting 5-Year Mortality After Surgery for Hepatocellular Carcinoma: A Nationwide Study

doi:10.1007/s11605-012-1986-3

CNU IR > Pharmacy and Science > Dept. of Pharmacy > Periodical Articles > Item 310902800/27538

Please use this identifier to cite or link to this item: https://ir.cnu.edu.tw/handle/310902800/27538

Title:	Artificial Neural Network Model for Predicting 5-Year Mortality After Surgery for Hepatocellular Carcinoma: A Nationwide Study
Authors:	Shi, Hon-Yi Lee, King-Teh Wang, Jhi-Joung Sun, Ding-Ping Lee, Hao-Hsien Chiu, Chong-Chi
Contributors:	藥學系
Keywords:	Hepatocellular Carcinoma Artificial Neural Network Logistic Regression Five-Year Mortality Rate
Date:	2012-11
Issue Date:	2014-03-21 16:12:48 (UTC+8)
Publisher:	Springer
Abstract:	To validate the use of artificial neural network (ANN) models for predicting 5-year mortality in HCC and to compare their predictive capability with that of logistic regression (LR) models.This study retrospectively compared LR and ANN models based on initial clinical data for 22,926 HCC surgery patients from 1998 to 2009. A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and to rank the importance of variables.Compared to the LR models, the ANN models had a better accuracy rate in 96.57 % of cases, a better Hosmer-Lemeshow statistic in 0.34 of cases, and a better receiver operating characteristic curves in 88.51 % of cases. Surgeon volume was the most influential (sensitive) parameter affecting 5-year mortality followed by hospital volume and Charlson co-morbidity index.In comparison with the conventional LR model, the ANN model in this study was more accurate in predicting 5-year mortality. 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.
Relation:	Journal of Gastrointestinal Surgery, 16(11), 2126-2131
Appears in Collections:	[Dept. of Pharmacy] Periodical Articles

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