Business School, Sichuan University, Chengdu, China.
School of Labor and Human Resources, Renmin University of China, Beijing, China.
BMC Med Inform Decis Mak. 2021 Dec 30;21(1):371. doi: 10.1186/s12911-021-01731-3.
For liver cancer patients, the occurrence of postoperative complications increases the difficulty of perioperative nursing, prolongs the hospitalization time of patients, and leads to large increases in hospitalization costs. The ability to identify influencing factors and to predict the risk of complications in patients with liver cancer after surgery could assist doctors to make better clinical decisions.
The aim of the study was to develop a postoperative complication risk prediction model based on machine learning algorithms, which utilizes variables obtained before or during the liver cancer surgery, to predict when complications present with clinical symptoms and the ways of reducing the risk of complications.
The study subjects were liver cancer patients who had undergone liver resection. There were 175 individuals, and 13 variables were recorded. 70% of the data were used for the training set, and 30% for the test set. The performance of five machine learning models, logistic regression, decision trees-C5.0, decision trees-CART, support vector machines, and random forests, for predicting postoperative complication risk in liver resection patients were compared. The significant influencing factors were selected by combining results of multiple methods, based on which the prediction model of postoperative complications risk was created. The results were analyzed to give suggestions of how to reduce the risk of complications.
Random Forest gave the best performance from the decision curves analysis. The decision tree-C5.0 algorithm had the best performance of the five machine learning algorithms if ACC and AUC were used as evaluation indicators, producing an area under the receiver operating characteristic curve value of 0.91 (95% CI 0.77-1), with an accuracy of 92.45% (95% CI 85-100%), the sensitivity of 87.5%, and specificity of 94.59%. The duration of operation, patient's BMI, and length of incision were significant influencing factors of postoperative complication risk in liver resection patients.
To reduce the risk of complications, it appears to be important that the patient's BMI should be above 22.96 before the operation, and the duration of the operation should be minimized.
对于肝癌患者来说，术后并发症的发生增加了围手术期护理的难度，延长了患者的住院时间，并导致住院费用大幅增加。识别影响因素并预测肝癌患者术后并发症的风险，可帮助医生做出更好的临床决策。
本研究旨在基于机器学习算法，建立一个术后并发症风险预测模型，利用肝癌手术前或手术期间获得的变量，预测并发症出现临床症状的时间以及降低并发症风险的方法。
研究对象为接受肝切除术的肝癌患者，共 175 例，记录了 13 个变量。70%的数据用于训练集，30%用于测试集。比较了逻辑回归、决策树-C5.0、决策树-CART、支持向量机和随机森林这 5 种机器学习模型对肝切除患者术后并发症风险预测的性能。结合多种方法的结果，选择显著影响因素，建立术后并发症风险预测模型。并对结果进行分析，提出降低并发症风险的建议。
从决策曲线分析来看，随机森林的表现最佳。如果以 ACC 和 AUC 作为评价指标，决策树-C5.0 算法在 5 种机器学习算法中的性能最佳，产生的受试者工作特征曲线下面积值为 0.91（95%CI 0.77-1），准确率为 92.45%（95%CI 85-100%），敏感度为 87.5%，特异度为 94.59%。手术时间、患者 BMI 和切口长度是肝切除患者术后并发症风险的显著影响因素。
为降低并发症风险，患者 BMI 术前应高于 22.96，且应尽量缩短手术时间。
