Wei Zijian, Li Jing, Li Xueyan, Zhao Yuzhuo, Jia Lijing, Li Tanshi
Department of School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China.
Management School, Beijing Union University, Beijing 100101, China.
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2019 Mar;31(3):359-362. doi: 10.3760/cma.j.issn.2095-4352.2019.03.020.
To propose a method of prediction for fatal gastrointestinal bleeding recurrence in hospital and a method of feature selection via machine learning models.
728 digestive tract hemorrhage samples were extracted from the first aid database of PLA General Hospital, and 343 patients among them were diagnosed as fatal gastrointestinal bleeding recurrence in hospital. A total of 64 physiological or laboratory indicators were extracted and screened. Based on the ten-fold cross-validation, Logistic regression, AdaBoost and XGBoost were used for classification prediction and comparison. XGBoost was used to search sequence features, and the key indicators for predicting fatal gastrointestinal bleeding recurrence in hospital were screened out according to the importance of the indicators during training.
Logistic regression, AdaBoost and XGBoost all get better F1.5 score under each feature input dimension, among which XGBoost had the best effect and the highest score, which was able to identify as many patients as possible who might have fatal gastrointestinal bleeding recurrence in hospital. Through XGBoost iteration results, the Top 30 indicators with high importance for predicting fatal gastrointestinal bleeding recurrence in hospital were ranked. The F1.5 scores of the first 12 key indicators peaked at iteration (0.893), including hemoglobin (Hb), calcium (CA), red blood cell count (RBC), mean platelet volume (MPV), mean erythrocyte hemoglobin concentration (MCH), systolic blood pressure (SBP), platelet count (PLT), magnesium (MG), lymphocyte (LYM), glucose (GLU, blood gas analysis), glucose (GLU, blood biochemistry) and diastolic blood pressure (DBP).
Logistic regression, AdaBoost and XGBoost could achieve the purpose of early warning for predicting fatal gastrointestinal bleeding recurrence in hospital, and XGBoost is the most suitable. The 12 most important indicators were screened out by sequential forward selection.
提出一种医院内致命性胃肠道出血复发的预测方法以及一种通过机器学习模型进行特征选择的方法。
从解放军总医院急救数据库中提取728例消化道出血样本，其中343例患者被诊断为医院内致命性胃肠道出血复发。共提取并筛选了64项生理或实验室指标。基于十折交叉验证，使用逻辑回归、AdaBoost和XGBoost进行分类预测和比较。使用XGBoost搜索序列特征，并根据训练过程中指标的重要性筛选出预测医院内致命性胃肠道出血复发的关键指标。
逻辑回归、AdaBoost和XGBoost在每个特征输入维度下均获得了较好的F1.5分数，其中XGBoost效果最佳、分数最高，能够识别出尽可能多的可能在医院发生致命性胃肠道出血复发的患者。通过XGBoost迭代结果，对预测医院内致命性胃肠道出血复发具有高重要性的前30项指标进行了排序。前12项关键指标的F1.5分数在迭代时达到峰值（0.893），包括血红蛋白（Hb）、钙（CA）、红细胞计数（RBC）、平均血小板体积（MPV）、平均红细胞血红蛋白浓度（MCH）、收缩压（SBP）、血小板计数（PLT）、镁（MG）、淋巴细胞（LYM）、葡萄糖（GLU，血气分析）、葡萄糖（GLU，血液生化）和舒张压（DBP）。
逻辑回归、AdaBoost和XGBoost均可实现对医院内致命性胃肠道出血复发进行预警预测的目的，且XGBoost最为合适。通过顺序向前选择筛选出了12项最重要的指标。
