Prediction Model of Intensive Care Unit Length of Stay for Patients with Cardiac Surgery

被引:0
|
作者
Zhang P. [1 ,2 ,3 ]
Wu N. [1 ,2 ,3 ]
Zhang H. [1 ,2 ,3 ]
Li G. [1 ,2 ,3 ]
Liu J. [1 ,2 ,3 ]
Li K. [1 ,2 ,3 ]
机构
[1] School of Life Science & Technology, University of Electronic Science & Technology of China, Chengdu
[2] West China Medical School, Sichuan University, Chengdu
[3] The People's Hospital of Pingshan in Sichuan Province, Yibin
关键词
Cardiac surgery; Intensive care unit; Length of stay; Machine learning;
D O I
10.12178/1001-0548.2022004
中图分类号
学科分类号
摘要
The analysis and prediction of influencing factors of length of stay in intensive care unit (ICU) of cardiac surgery patients is conducive to the early intervention and cost control of inpatients, and is of great significance to the treatment and nursing of cardiac surgery patients. This paper uses the intensive care database medical information mart for intensive care IV (MIMIC-IV) as the experimental data set, 7567 patients were included. 41 important predictors were selected from 126 influencing factors by least absolute shrinkage and selection operator (Lasso). This paper constructs a prediction model of length of stay in cardiac surgery intensive care unit based on gradient enhanced decision tree (GBDT) algorithm. The experimental results show that under the condition of training all predictors, the average accuracy of GBDT model is 0.688 higher than that of traditional logistic regression algorithm, which is 0.603. The GBDT algorithm with the selected important predictors has the same effect on the final average accuracy as that with all factors, which shows that this method can optimize data collection, accurately predict length of stay in ICU, and provide algorithm support for clinical decision support system. © 2022, Editorial Board of Journal of the University of Electronic Science and Technology of China. All right reserved.
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页码:500 / 505
页数:5
相关论文
共 21 条
  • [1] HARISH RAMAKRISHNA, The high-cardiovascular risk patient for non-cardiac surgery: Guidelines for the perioperative clinician, A New Era in Cardiology Research and Therapy-BIT's 8th Annual International Congress of Cardiology (ICC-2022), pp. 194-195, (2016)
  • [2] ZENG S, LIN L, CHEN C., Research progress of the perioperative application of dexmedetomidine in cardiac surgery, Medical Recapitulate, 24, 6, pp. 1217-1223, (2018)
  • [3] ELY E W, GAUTAM S, MARGOLIN R, Et al., The impact of delirium in the intensive care unit on hospital length of stay, Intensive Care Med, 27, pp. 1892-1900, (2001)
  • [4] VINCENT J L, SINGER M., Critical care: Advances and future perspectives, Lancet, 376, 9749, pp. 1354-1361, (2010)
  • [5] APPELROS P., Prediction of length of stay for stroke patients, Acta Neurologica Scandinavica, 9, pp. 15-19, (2007)
  • [6] FILIPE P, MANUEL F S, lVARO S, Et al., Adoption of pervasive intelligent information systems in intensive medicine, Procedia Technology, 9, 4, pp. 1022-1032, (2013)
  • [7] LEO A C, ROGER R M, DAVID J S, Et al., Big data" in the intensive care unit. Closing the data loop, American Journal of Respiratory and Critical Care Medicine, 187, 11, pp. 1157-1160, (2013)
  • [8] SYED W A S, YU J J, MOON H J, Et al., A machine learning-based model for 1-year mortality prediction in patients admitted to an intensive care unit with a diagnosis of sepsis, Medicina Intensiva, 44, 3, pp. 160-170, (2020)
  • [9] BUCHMAN T G, KUBOS K L, SEIDLER A J, Et al., A comparison of statistical and connectionist models for the prediction of chronicity in a surgical intensive care unit, Critical Care Medicine, 22, 5, pp. 750-751, (1994)
  • [10] LIN Q J., Factors influencing ICU treatment time in patients with severe infection, Practical Clinical Medicine, 18, 4, pp. 24-25, (2017)