Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (19): 156-163.DOI: 10.3778/j.issn.1002-8331.2006-0205

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Analytical Model for Correlations Between Indicators and Critical Illness

ZHANG Lige, CHEN Yuwen, QIN Xiaolin, YI Bin, LI Yujie   

  1. 1.Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu 610041, China
    2.University of Chinese Academy of Sciences, Beijing 100049, China
    3.Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
    4.The First Affiliated Hospital, Army Medical University, Chongqing 400038, China
  • Online:2021-10-01 Published:2021-09-29



  1. 1.中国科学院 成都计算机应用研究所,成都 610041
    2.中国科学院大学,北京 100049
    3.中国科学院 重庆绿色智能技术研究院,重庆 400714
    4.陆军军医大学 第一附属医院,重庆 400038


The selection of preoperative and intraoperative indicators affects the real-time capability and accuracy of prediction for critical illness. At present, there are so many kinds of preoperative and intraoperative indicators that it is difficult to find the potential relationship between them and critical illness. Aiming at the correlations between indicators of patients and critical illness, an analysis model based on machine learning is proposed. The model combines statistical method and Spearman’s rank correlation coefficient to remove redundant indicators. Then, the XGBoost model is used to analyze the contribution of each indicator in predicting critical illness and the contribution is taken as the correlations between each indicator and critical illness. Finally, key indicators of critical illness are selected according to the correlations between each indicator and critical illness. Preoperative and intraoperative data of liver failure and renal failure are used to verify the model. The results show that the model can effectively analyze the correlations between indicators and critical illness, and the key indicators extracted by this model are slightly more effective than all indicators in the prediction of critical illness.

Key words: prediction of critical illness, Spearman’s rank correlation coefficient, XGBoost, analysis of indicators



关键词: 危重症预测, 斯皮尔曼等级相关系数, XGBoost, 指标分析