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Abstract：
BACKGROUND: Femoral neck fracture (FNF) accounts for approximately 3.58% of all fractures in the entire body, exhibiting an increasing trend each year. According to a survey, in 1990, the total number of hip fractures in men and women worldwide was approximately 338,000 and 917,000, respectively. In China, FNFs account for 48.22% of hip fractures. Currently, many studies have been conducted on postdischarge mortality and mortality risk in patients with FNF. However, there have been no definitive studies on in-hospital mortality or its influencing factors in patients with severe FNF admitted to the intensive care unit.
OBJECTIVE: In this paper, 3 machine learning methods were used to construct a nosocomial death prediction model for patients admitted to intensive care units to assist clinicians in early clinical decision-making.
METHODS: A retrospective analysis was conducted using information of a patient with FNF from the Medical Information Mart for Intensive Care III. After balancing the data set using the Synthetic Minority Oversampling Technique algorithm, patients were randomly separated into a 70% training set and a 30% testing set for the development and validation, respectively, of the prediction model. Random forest, extreme gradient boosting, and backpropagation neural network prediction models were constructed with nosocomial death as the outcome. Model performance was assessed using the area under the receiver operating characteristic curve, accuracy, precision, sensitivity, and specificity. The predictive value of the models was verified in comparison to the traditional logistic model.
RESULTS: A total of 366 patients with FNFs were selected, including 48 cases (13.1%) of in-hospital death. Data from 636 patients were obtained by balancing the data set with the in-hospital death group to survival group as 1:1. The 3 machine learning models exhibited high predictive accuracy, and the area under the receiver operating characteristic curve of the random forest, extreme gradient boosting, and backpropagation neural network were 0.98, 0.97, and 0.95, respectively, all with higher predictive performance than the traditional logistic regression model. Ranking the importance of the feature variables, the top 10 feature variables that were meaningful for predicting the risk of in-hospital death of patients were the Simplified Acute Physiology Score II, lactate, creatinine, gender, vitamin D, calcium, creatine kinase, creatine kinase isoenzyme, white blood cell, and age.
CONCLUSIONS: Death risk assessment models constructed using machine learning have positive significance for predicting the in-hospital mortality of patients with severe disease and provide a valid basis for reducing in-hospital mortality and improving patient prognosis.
OBJECTIVE: In this paper, 3 machine learning methods were used to construct a nosocomial death prediction model for patients admitted to intensive care units to assist clinicians in early clinical decision-making.
METHODS: A retrospective analysis was conducted using information of a patient with FNF from the Medical Information Mart for Intensive Care III. After balancing the data set using the Synthetic Minority Oversampling Technique algorithm, patients were randomly separated into a 70% training set and a 30% testing set for the development and validation, respectively, of the prediction model. Random forest, extreme gradient boosting, and backpropagation neural network prediction models were constructed with nosocomial death as the outcome. Model performance was assessed using the area under the receiver operating characteristic curve, accuracy, precision, sensitivity, and specificity. The predictive value of the models was verified in comparison to the traditional logistic model.
RESULTS: A total of 366 patients with FNFs were selected, including 48 cases (13.1%) of in-hospital death. Data from 636 patients were obtained by balancing the data set with the in-hospital death group to survival group as 1:1. The 3 machine learning models exhibited high predictive accuracy, and the area under the receiver operating characteristic curve of the random forest, extreme gradient boosting, and backpropagation neural network were 0.98, 0.97, and 0.95, respectively, all with higher predictive performance than the traditional logistic regression model. Ranking the importance of the feature variables, the top 10 feature variables that were meaningful for predicting the risk of in-hospital death of patients were the Simplified Acute Physiology Score II, lactate, creatinine, gender, vitamin D, calcium, creatine kinase, creatine kinase isoenzyme, white blood cell, and age.
CONCLUSIONS: Death risk assessment models constructed using machine learning have positive significance for predicting the in-hospital mortality of patients with severe disease and provide a valid basis for reducing in-hospital mortality and improving patient prognosis.
摘要：
背景：股骨颈骨折（FNF）约占全身所有骨折的3.58％，呈现逐年增长的趋势。根据一项调查,1990年,全世界男性和女性的髋部骨折总数分别约为338,000和917,000.在中国,FNFs占髋部骨折的48.22%。目前,已经对FNF患者的出院后死亡率和死亡风险进行了许多研究.然而,目前尚无关于重症监护病房重症FNF患者院内死亡率及其影响因素的确切研究.
目的：在本文中，采用3种机器学习方法构建重症监护病房患者院内死亡预测模型,以辅助临床医师早期临床决策。
方法：使用来自重症监护医学信息集市III的FNF患者的信息进行回顾性分析。在使用合成少数过采样技术算法平衡数据集之后，患者随机分为70％的训练集和30％的测试集进行开发和验证，分别,预测模型。随机森林,极端梯度增强，并以医院死亡为结果构建反向传播神经网络预测模型。使用接收器工作特性曲线下的面积评估模型性能，准确度,精度,灵敏度,和特异性。通过与传统logistic模型的对比,验证了模型的预测价值。
结果：共选择366名FNFs患者，其中48例（13.1%）住院死亡。通过将数据集与院内死亡组和生存组的平衡为1:1来获得来自636名患者的数据。3种机器学习模型表现出很高的预测精度，和随机森林的接收器工作特性曲线下的面积，极端梯度增强，和反向传播神经网络分别为0.98、0.97和0.95，均具有比传统逻辑回归模型更高的预测性能。对特征变量的重要性进行排名，对预测患者院内死亡风险有意义的前10个特征变量是简化急性生理学评分II,乳酸，肌酐,性别,维生素D,钙，肌酸激酶，肌酸激酶同工酶，白细胞,和年龄。
结论：利用机器学习构建的死亡风险评估模型对预测重症患者院内死亡率具有积极意义，为降低院内死亡率、改善患者预后提供有效依据。
目的：在本文中，采用3种机器学习方法构建重症监护病房患者院内死亡预测模型,以辅助临床医师早期临床决策。
方法：使用来自重症监护医学信息集市III的FNF患者的信息进行回顾性分析。在使用合成少数过采样技术算法平衡数据集之后，患者随机分为70％的训练集和30％的测试集进行开发和验证，分别,预测模型。随机森林,极端梯度增强，并以医院死亡为结果构建反向传播神经网络预测模型。使用接收器工作特性曲线下的面积评估模型性能，准确度,精度,灵敏度,和特异性。通过与传统logistic模型的对比,验证了模型的预测价值。
结果：共选择366名FNFs患者，其中48例（13.1%）住院死亡。通过将数据集与院内死亡组和生存组的平衡为1:1来获得来自636名患者的数据。3种机器学习模型表现出很高的预测精度，和随机森林的接收器工作特性曲线下的面积，极端梯度增强，和反向传播神经网络分别为0.98、0.97和0.95，均具有比传统逻辑回归模型更高的预测性能。对特征变量的重要性进行排名，对预测患者院内死亡风险有意义的前10个特征变量是简化急性生理学评分II,乳酸，肌酐,性别,维生素D,钙，肌酸激酶，肌酸激酶同工酶，白细胞,和年龄。
结论：利用机器学习构建的死亡风险评估模型对预测重症患者院内死亡率具有积极意义，为降低院内死亡率、改善患者预后提供有效依据。
