基于集成学习模型预测重症患者再入重症监护病房的风险

doi:10.19723/j.issn.1671-167X.2021.03.021

北京大学学报（医学版） ›› 2021, Vol. 53 ›› Issue (3): 566-572. doi: 10.19723/j.issn.1671-167X.2021.03.021

基于集成学习模型预测重症患者再入重症监护病房的风险

林瑜^1,²,吴静依³,蔺轲^1,²,胡永华^2,⁴,孔桂兰^1,^3,^Δ()

1.北京大学健康医疗大数据国家研究院,北京 100191
2.北京大学公共卫生学院流行病与卫生统计系,北京 100191
3.北京大学信息技术高等研究院,杭州 311215
4.北京大学医学信息学中心,北京 100191

收稿日期:2019-07-05 出版日期:2021-06-18 发布日期:2021-06-16
通讯作者: 孔桂兰 E-mail:guilan.kong@hsc.pku.edu.cn
基金资助:
国家自然科学基金(81771938);国家自然科学基金(91846101);北京市自然科学基金(7212201);北京大学医学部-密歇根大学医学院转化医学与临床研究联合研究所项目(BMU2020JI011)

Prediction of intensive care unit readmission for critically ill patients based on ensemble learning

LIN Yu^1,²,WU Jing-yi³,LIN Ke^1,²,HU Yong-hua^2,⁴,KONG Gui-lan^1,^3,^Δ()

1. National Institute of Health Data Science, Peking University, Beijing 100191, China
2. Department of Epidemiology and Biostatistics, Peking University School of Public Health, Beijing 100191, China
3. Advanced Institute of Information Technology, Peking University, Hangzhou 311215, China
4. Peking University Medical Informatics Center, Beijing 100191, China

Received:2019-07-05 Online:2021-06-18 Published:2021-06-16
Contact: Gui-lan KONG E-mail:guilan.kong@hsc.pku.edu.cn
Supported by:
National Natural Science Foundation of China(81771938);National Natural Science Foundation of China(91846101);Beijing Municipal Natural Science Foundation(7212201);Project of the University of Michigan Health System-Peking University Health Science Center Joint Institute for Translational and Clinical Research BMU(BMU2020JI011)

摘要/Abstract

摘要：

目的: 基于集成学习算法建立患者再入重症监护病房(intensive care unit, ICU)的风险预测模型,并比较各个模型的预测性能。方法: 使用美国重症医学数据库(medical information mart for intensive care,MIMIC)-Ⅲ,根据纳入、排除标准筛选患者,提取人口学特征、生命体征、实验室检查、合并症等可能对结局有预测作用的变量,基于集成学习方法随机森林、自适应提升算法(adaptive boosting, AdaBoost)和梯度提升决策树(gradient boosting decision tree, GBDT)建立再入ICU预测模型,并比较集成学习与Logistic回归的预测性能。使用五折交叉验证后的平均灵敏度、阳性预测值、阴性预测值、假阳性率、假阴性率、受试者工作特征曲线下面积(area under the receiver operating characteristic curve,AUROC)和Brier评分评价模型效果,基于最佳性能模型给出重要性排序前10位的预测变量。结果: 所有模型中,GBDT (AUROC=0.858)优于随机森林(AUROC=0.827),略好于AdaBoost (AUROC=0.851)。与Logistic回归(AUROC=0.810)相比,集成学习算法在区分度上均有较大的提升。GBDT算法给出的变量重要性排序中,平均动脉压、收缩压、舒张压、心率、尿量、血肌酐等变量排序靠前,相对而言,再入ICU患者的心血管功能和肾功能更差。结论: 基于集成学习算法的患者再入ICU预测模型表现出较好的性能,优于Logistic回归。使用集成学习算法建立的再入ICU风险预测模型可用于识别再入ICU风险高的患者,医务人员可针对高风险患者采取干预措施,改善患者的整体临床结局。

关键词: 重症监护病房, 病人再入院, 机器学习, 试验预期值

Abstract:

Objective: To develop machine learning models for predicting intensive care unit (ICU) readmission using ensemble learning algorithms. Methods: A publicly accessible American ICU database, medical information mart for intensive care (MIMIC)-Ⅲ as the data source was used, and the patients were selected by the inclusion and exclusion criteria. A set of variables that had the predictive ability of outcome including demographics, vital signs, laboratory tests, and comorbidities of patients were extracted from the dataset. We built the ICU readmission prediction models based on ensemble learning methods including random forest, adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT), and compared the prediction performance of the machine learning models with a conventional Logistic regression model. Five-fold cross validation was used to train and validate the prediction models. Average sensitivity, positive prediction value, negative prediction value, false positive rate, false negative rate, area under the receiver operating characteristic curve (AUROC) and Brier score were used as performance measures. After constructing the prediction models, top 10 predictive variables based on importance ranking were identified by the model with the best discrimination. Results: Among these ICU readmission prediction models, GBDT (AUROC=0.858) had better performance than random forest (AUROC=0.827), and was slightly superior to AdaBoost (AUROC=0.851) in terms of AUROC. Compared with Logistic regression (AUROC=0.810), the discrimination of the three ensemble learning models was much better. The feature importance provided by GBDT showed that the top ranking variables included vital signs and laboratory tests. The patients with ICU readmission had higher mean arterial pressure, systolic blood pressure, diastolic blood pressure, and heart rate than the patients without ICU readmission. Meanwhile, the patients readmitted to ICU experienced lower urine output and higher serum creatinine. Overall, the patients having repeated admissions during their hospitalization showed worse heart function and renal function compared with the patients without ICU readmission. Conclusion: The ensemble learning based ICU readmission prediction models had better performance than Logistic regression model. Such ensemble learning models have the potential to aid ICU physicians in identifying those patients with high risk of ICU readmission and thus help improve overall clinical outcomes.

Key words: Intensive care units, Patient readmission, Machine learning, Predictive value of tests

中图分类号:

R459.7

林瑜,吴静依,蔺轲,胡永华,孔桂兰. 基于集成学习模型预测重症患者再入重症监护病房的风险[J]. 北京大学学报（医学版）, 2021, 53(3): 566-572.

LIN Yu,WU Jing-yi,LIN Ke,HU Yong-hua,KONG Gui-lan. Prediction of intensive care unit readmission for critically ill patients based on ensemble learning[J]. Journal of Peking University (Health Sciences), 2021, 53(3): 566-572.

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参考文献 20

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Classifier	Accuracy
Classifier	Random under-sampling	NearMiss
Logistic regression	0.615	0.838
Random forest	0.542	0.844
AdaBoost	0.620	0.873
GBDT	0.626	0.874

Characteristics	Total	Patients with ICU readmission	Patients without ICU readmission	P value
Age^b/years, $\bar{x} \pm s$	62.67±16.22	64.08±15.16	62.58±16.29	<0.001
Gender (female)^a, n (%)	11 319 (42.38)	671 (41.69)	10 648 (42.49)	0.16
ICU stay^b/d, $\bar{x} \pm s$	4.56±5.60	5.46±6.12	4.50±5.56	<0.001
GCS total score^b, $\bar{x} \pm s$	14.33±1.42	14.17±1.58	14.34±1.41	<0.001
GCS motor^b, $\bar{x} \pm s$	5.89±0.50	5.88±0.50	5.89±0.50	0.08
GCS verbal^b, $\bar{x} \pm s$	4.41±1.28	4.40±1.18	4.41±1.29	<0.001
GCS eyes^b, $\bar{x} \pm s$	3.75±0.54	3.73±0.56	3.75±0.54	0.10
Admission type^a, n (%)
Medical	18 208 (68.17)	1 054 (63.92)	17 154 (68.45)
Scheduled surgery	3 620 (13.55)	179 (10.86)	3 441 (13.73)	<0.001
Unscheduled surgery	4 881 (18.27)	416 (25.23)	4 465 (17.82)

Performance	Logistic regression	Random forest	AdaBoost	GBDT
Sensitivity	0.763±0.029	0.787±0.010	0.821±0.029	0.817±0.028
PPV	0.843±0.022	0.858±0.056	0.876±0.040	0.892±0.038
NPV	0.784±0.018	0.802±0.012	0.832±0.020	0.832±0.017
FPR	0.143±0.026	0.134±0.057	0.120±0.044	0.101±0.040
FNR	0.237±0.029	0.213±0.010	0.179±0.029	0.183±0.028
AUROC	0.810±0.013	0.827±0.029	0.851±0.017	0.858±0.013
Brier score	0.190±0.013	0.173±0.029	0.149±0.017	0.142±0.013

Rank	Variables	Importance	With ICU readmission, $\bar{x} \pm s$	Without ICU readmission, $\bar{x} \pm s$
1	Platelet/(×10³/μL)	0.109	240.05±154.37	230.05±131.88
2	Glucose (maximum)/(mg/dL)	0.093	208.36±109.04	199.68±104.80
3	Urine output/mL	0.089	1 908.70±1 111.36	2 089.51±1 129.90
4	Mean arterial pressure (maximum)/mmHg	0.067	122.04±43.03	116.87±38.68
5	Glucose (minimum)/(mg/dL)	0.058	96.06±33.03	99.83±35.78
6	Heart rate (maximum)/(beat/min)	0.058	114.38±25.07	109.35±23.49
7	Creatinine/(mg/dL)	0.053	1.33±1.24	1.19±1.23
8	Systolic blood pressure (maximum)/mmHg	0.042	160.42±29.69	158.04±28.14
9	Diastolic blood pressure (minimum)/mmHg	0.035	40.47±12.59	41.99±12.85
10	Heart rate (minimum)/(beat/min)	0.031	68.79±15.12	68.30±14.46

基于集成学习模型预测重症患者再入重症监护病房的风险

Prediction of intensive care unit readmission for critically ill patients based on ensemble learning

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