Machine Learning-based Prediction of Postoperative 30-days Mortality

Linna Wang, Linji Li, T. Zhu, Congli Ma, Li Lu
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引用次数: 1

Abstract

Surgical patients aged 65 and over are facing a 2-10 times higher risk of death after surgery. Early prediction of postoperative mortality is essential, as timely and appropriate treatment can improve survival outcomes. With the development of medical and computer technology, numerous available health-related data can be recorded for research. Among various patient indicators which may affect the accuracy of prediction, it is necessary to find highly relevant and efficient features. The aims of this study were to use machine learning algorithms, specifically Bagging and Boosting Algorithms (e.g. Random Forest, eXtreme Gradient Boosting), to predict the postoperative 30-days mortality in surgical patients aged over 65, and to identify the optimal features using genetic algorithm(GA). This prospective study was developed and validated on the cohort from electronic health records (EHRs) of West China Hospital, Sichuan University, which contained 7467 surgical patients (0.924% mortality rate) who underwent surgery between July 1, 2019 and October 31, 2020. Compared with models like the traditional logistic regression model and the baseline ASA physical status, We found that XGBoost with hyper-parameters had best performance based solely on the automatically obtained features (area under the curve [AUC] of 0.9318, 95% confidence interval [CI] 0.9041 - 0.9594). The AUC of baseline ASA-PS was 0.6787 (95% CI 0.6471 - 0.7103) using XGBoost. When both ASA-PS and the selected features are included as inputs, XGboost achieved the AUC of 0.9345 (95% CI 0.9076 - 0.9613).
基于机器学习的术后30天死亡率预测
65岁及以上的手术患者在手术后面临2-10倍的死亡风险。早期预测术后死亡率至关重要,因为及时和适当的治疗可以改善生存结果。随着医学和计算机技术的发展,可以记录大量可用的健康相关数据以供研究。在各种可能影响预测准确性的患者指标中,有必要找到高度相关和高效的特征。本研究的目的是使用机器学习算法,特别是Bagging和Boosting算法(例如Random Forest, eXtreme Gradient Boosting)来预测65岁以上手术患者术后30天死亡率,并使用遗传算法(GA)识别最佳特征。本前瞻性研究基于四川大学华西医院电子健康档案(EHRs)中的队列进行开发和验证,该队列包含2019年7月1日至2020年10月31日期间接受手术的7467例手术患者(死亡率0.924%)。与传统逻辑回归模型和基线ASA物理状态等模型相比,我们发现仅基于自动获得的特征(曲线下面积[AUC]为0.9318,95%置信区间[CI]为0.9041 ~ 0.9594),超参数XGBoost具有最佳性能。使用XGBoost时基线ASA-PS的AUC为0.6787 (95% CI 0.6471 - 0.7103)。当ASA-PS和所选特征都作为输入时,XGboost的AUC为0.9345 (95% CI 0.9076 - 0.9613)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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