A Comparison of Micu Survival Prediction Using the Logistic Regression Model and Artificial Neural Network Model

Abstract

在醫療費用支出緊縮的政策下，隨著醫療技術的發展與人口老化的雙重壓力下，將可 預見重症醫療照護對有限醫療資源將造成更大的壓力。因此本研究的目的係比較邏輯 迴歸與類神經網路二種模式，對內科加護病房病人存活率之預測能力，提供一更倫理 與客觀的存活率預測系統，以進一步促使內科加護病房資源能更有效率之營運。此二 個模式使用於2002年1月至2004年1月期間住進台灣某醫學中心內科加護病房1,496位 病人的APACHE-II(Acute Physiology and Chronic Health Evaluation-II)及GCS( Glasgow Coma Scale)分數來進行存活率之預測。研究結果顯示類神經網路模式相較 於邏輯迴歸模式在存活者(86. 7%,n=361)與整體病患(74.7%,n=498)之預測能力均較 佳。 Under the policy of restraint in medical expenditure and with the dual pressures of medical technology development and population aging, the critical care services will exert even greater pressure on the limited medical resources. Therefore, the objective of this study is to compare the abilities of two models, the Logistic Regression Model and the Neural Network Model, to predict the survival of critical care patients, in order to provide a more ethical and objective survival prediction system, as well as to promote more effective management of the resources of the medical intensive care unit (MICU). The two models use the Acute Physiology and Chronic Health Evaluation-II (APACHE-II ) and Glasgow Coma Scale (GCS) scores of 1,496 patients stayed who in the MICU of a Taiwan medical center during January 2002-January 2004 to conduct the survival prediction . The study results show that the Neural Network Model has a better predictive ability than the Logistic Regression Model both with regard to the survivors (86.7%, n=361) and with regard to the entire population of patients studied (74 .7%, n=498).