医学
机器学习
计算器
预测值
人工智能
接收机工作特性
美国麻醉师学会
外科
内科学
计算机科学
操作系统
作者
Jeff Gao,Aziz M. Merchant
标识
DOI:10.1177/00031348211038568
摘要
There is a significant mortality burden associated with emergency general surgery (EGS) procedures. The objective of this study was to develop and validate the use of a machine learning approach to predict mortality following EGS.The American College of Surgeons National Surgical Quality Improvement Program database was queried for patients who underwent EGS between 2012 and 2017. We developed a machine learning algorithm to predict mortality following EGS and compared its performance with existing risk-prediction models of American Society of Anesthesiologists (ASA) classification, American College of Surgeon Surgical Risk Calculator (ACS-SRC), and the modified frailty index (mFI) using the area under receiver operative curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).The machine learning algorithm had a very high performance for predicting mortality following EGS, and it had superior performance compared to the ASA classification, ACS-SRC, and the mFI, as measured by the AUC, sensitivity, specificity, PPV, and NPV.Machine learning approaches may be a promising tool to predict outcomes for EGS, aiding clinicians in surgical decision-making and counseling of patients and family, improving clinical outcomes by identifying modifiable risk factors than can be optimized, and decreasing treatment costs through resource allocation.
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