機器學習在膿毒癥及危重癥患者預后評估中的應用_Chinese Journal of Respiratory and Critical Care Medicine

Authors：

路朋宇 ,  HUNAG Wei

大連醫科大學附屬第一醫院重癥醫學科（遼寧大連 116012）;

Corresponding?author：

HUNAG Wei, Email: huangwei9898@163.com

Keywords：

DOI：

10.7507/1671-6205.202304075

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Citation： HUNAG Wei. 機器學習在膿毒癥及危重癥患者預后評估中的應用. Chinese Journal of Respiratory and Critical Care Medicine, 2023, 22(9): 660-665. doi: 10.7507/1671-6205.202304075 Copy

1.	Singer M, Deutschman CS, Seymour CW, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA, 2016, 315(8): 801-810.
2.	Rudd KE, Johnson SC, Agesa KM, et al. Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study. Lancet, 2020, 395(10219): 200-211.
3.	Wang M, Jiang L, Zhu B, et al. The prevalence, risk factors, and outcomes of sepsis in critically ill patients in China: a multicenter prospective cohort study. Front Med (Lausanne), 2020, 7: 593808.
4.	Ben Ali W, Pesaranghader A, Avram R, et al. Implementing machine learning in interventional cardiology: the benefits are worth the trouble. Front Cardiovasc Med, 2021, 8: 711401.
5.	Johnson A, Bulgarelli L, Pollard T, et al. MIMIC-IV (version 2.2). PhysioNet, 2023.https://doi.org/10.13026/6mm1-ek67.
6.	Hu C, Li L, Li YM, et al. Explainable machine-learning model for prediction of in-hospital mortality in septic patients requiring intensive care unit readmission. Infect Dis Ther, 2022, 11(4): 1695-1713.
7.	Li SH, Dou RX, Song XD, et al. Developing an interpretable machine learning model to predict in-hospital mortality in sepsis patients: a retrospective temporal validation study. J Clin Med, 2023, 12(3): 915.
8.	Liu Y, Gao K, Deng HB, et al. A time-incorporated SOFA score-based machine learning model for predicting mortality in critically ill patients: a multicenter, real-world study. Int J Med Inform, 2022, 163: 104776.
9.	Li K, Shi QW, Liu SR, et al. Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree. Medicine (Baltimore), 2021, 100(19): e25813.
10.	Delgado R, Nú?ez-González JD, Yébenes JC, et al. Survival in the Intensive Care Unit A prognosis model based on Bayesian classifier. Artif Intell Med, 2021, 115: 102054.
11.	Jiang ZY, Bo LL, Xu ZH, et al. An explainable machine learning algorithm for risk factor analysis of in-hospital mortality in sepsis survivors with ICU readmission. Comput Methods Programs Biomed, 2021, 204: 106040.
12.	Ren N, Zhao X, Zhang X. Mortality prediction in ICU using a stacked ensemble model. Comput Math Methods Med, 2022, 2022: 3938492.
13.	Ghosh S, Li JY, Cao LB, et al. Septic shock prediction for ICU patients via coupled HMM walking on sequential contrast patterns. J Biomed Inform, 2017, 66: 19-31.
14.	Cheng CY, Kung CT, Chen FC, et al. Machine learning models for predicting in-hospital mortality in patient with sepsis Analysis of vital sign dynamics. Front Med (Lausanne), 2022, 9: 964667.
15.	Caicedo-Torres W, Gutierrez J. ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU. J Biomed Inform, 2019, 98: 103269.
16.	Wernly B, Mamandipoor B, Baldia P, et al. Machine learning predicts mortality in septic patients using only routinely available ABG variables a multi-centre evaluation. Int J Med Inform, 2021, 145: 104312.
17.	Chen YW, Li YJ, Deng P, et al. Learning to predict in-hospital mortality risk in the intensive care unit with attention-based temporal convolution network. BMC Anesthesiol, 2022, 22(1): 119.
18.	Awad A, Bader-El-Den M, McNicholas J, et al. Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach. Int J Med Inform, 2017, 108: 185-195.
19.	Purushotham S, Meng CZ, Che ZP, et al. Benchmarking deep learning models on large healthcare datasets. J Biomed Inform, 2018, 83: 112-134.
20.	Tang H, Jin ZC, Deng JJ, et al. Development and validation of a deep learning model to predict the survival of patients in ICU. J Am Med Inform Assoc, 2022, 29(9): 1567-1576.
21.	Ma XY, Wang M, Lin SH, et al. Knowledge and data-driven prediction of organ failure in critical care patients. Health Inf Sci Syst, 2023, 11(1): 7.
22.	Davoodi R, Moradi MH. Mortality prediction in intensive care units (ICUs) using a deep rule-based fuzzy classifier. J Biomed Inform, 2018, 79: 48-59.
23.	Chen WT, Long GD, Yao LN, et al. AMRNN: attended multi-task recurrent neural networks for dynamic illness severity prediction. World Wide Web, 2020, 23: 2753-2770.
24.	An Y, Liu Y, Chen XL, et al. TERTIAN: clinical endpoint prediction in ICU via Time-Aware Transformer-Based Hierarchical Attention Network. Comput Intell Neurosci, 2022, 2022: 4207940.
25.	Barboi C, Tzavelis A, Muhammad LN. Comparison of Severity of Illness Scores and Artificial Intelligence Models that are predictive of intensive care unit mortality: meta-analysis and review of the literature. JMIR Med Inform, 2022, 10(5): e35293.
26.	Hong N, Liu C, Gao JW, et al. State of the art of machine learning-enabled clinical decision support in intensive care units: literature review. JMIR Med Inform, 2022 , 10(3): e28781.
27.	Srivani M, Murugappan A, Mala T. Cognitive computing technological trends and future research directions in healthcare - a systematic literature review. Artif Intell Med, 2023, 138: 102513.
28.	Zeguendry A, Jarir Z, Quafafou M. Quantum machine learning: a review and case studies. Entropy (Basel), 2023, 25(2): 287.

1. Singer M, Deutschman CS, Seymour CW, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA, 2016, 315(8): 801-810.
2. Rudd KE, Johnson SC, Agesa KM, et al. Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study. Lancet, 2020, 395(10219): 200-211.
3. Wang M, Jiang L, Zhu B, et al. The prevalence, risk factors, and outcomes of sepsis in critically ill patients in China: a multicenter prospective cohort study. Front Med (Lausanne), 2020, 7: 593808.
4. Ben Ali W, Pesaranghader A, Avram R, et al. Implementing machine learning in interventional cardiology: the benefits are worth the trouble. Front Cardiovasc Med, 2021, 8: 711401.
5. Johnson A, Bulgarelli L, Pollard T, et al. MIMIC-IV (version 2.2). PhysioNet, 2023.https://doi.org/10.13026/6mm1-ek67.
6. Hu C, Li L, Li YM, et al. Explainable machine-learning model for prediction of in-hospital mortality in septic patients requiring intensive care unit readmission. Infect Dis Ther, 2022, 11(4): 1695-1713.
7. Li SH, Dou RX, Song XD, et al. Developing an interpretable machine learning model to predict in-hospital mortality in sepsis patients: a retrospective temporal validation study. J Clin Med, 2023, 12(3): 915.
8. Liu Y, Gao K, Deng HB, et al. A time-incorporated SOFA score-based machine learning model for predicting mortality in critically ill patients: a multicenter, real-world study. Int J Med Inform, 2022, 163: 104776.
9. Li K, Shi QW, Liu SR, et al. Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree. Medicine (Baltimore), 2021, 100(19): e25813.
10. Delgado R, Nú?ez-González JD, Yébenes JC, et al. Survival in the Intensive Care Unit A prognosis model based on Bayesian classifier. Artif Intell Med, 2021, 115: 102054.
11. Jiang ZY, Bo LL, Xu ZH, et al. An explainable machine learning algorithm for risk factor analysis of in-hospital mortality in sepsis survivors with ICU readmission. Comput Methods Programs Biomed, 2021, 204: 106040.
12. Ren N, Zhao X, Zhang X. Mortality prediction in ICU using a stacked ensemble model. Comput Math Methods Med, 2022, 2022: 3938492.
13. Ghosh S, Li JY, Cao LB, et al. Septic shock prediction for ICU patients via coupled HMM walking on sequential contrast patterns. J Biomed Inform, 2017, 66: 19-31.
14. Cheng CY, Kung CT, Chen FC, et al. Machine learning models for predicting in-hospital mortality in patient with sepsis Analysis of vital sign dynamics. Front Med (Lausanne), 2022, 9: 964667.
15. Caicedo-Torres W, Gutierrez J. ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU. J Biomed Inform, 2019, 98: 103269.
16. Wernly B, Mamandipoor B, Baldia P, et al. Machine learning predicts mortality in septic patients using only routinely available ABG variables a multi-centre evaluation. Int J Med Inform, 2021, 145: 104312.
17. Chen YW, Li YJ, Deng P, et al. Learning to predict in-hospital mortality risk in the intensive care unit with attention-based temporal convolution network. BMC Anesthesiol, 2022, 22(1): 119.
18. Awad A, Bader-El-Den M, McNicholas J, et al. Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach. Int J Med Inform, 2017, 108: 185-195.
19. Purushotham S, Meng CZ, Che ZP, et al. Benchmarking deep learning models on large healthcare datasets. J Biomed Inform, 2018, 83: 112-134.
20. Tang H, Jin ZC, Deng JJ, et al. Development and validation of a deep learning model to predict the survival of patients in ICU. J Am Med Inform Assoc, 2022, 29(9): 1567-1576.
21. Ma XY, Wang M, Lin SH, et al. Knowledge and data-driven prediction of organ failure in critical care patients. Health Inf Sci Syst, 2023, 11(1): 7.
22. Davoodi R, Moradi MH. Mortality prediction in intensive care units (ICUs) using a deep rule-based fuzzy classifier. J Biomed Inform, 2018, 79: 48-59.
23. Chen WT, Long GD, Yao LN, et al. AMRNN: attended multi-task recurrent neural networks for dynamic illness severity prediction. World Wide Web, 2020, 23: 2753-2770.
24. An Y, Liu Y, Chen XL, et al. TERTIAN: clinical endpoint prediction in ICU via Time-Aware Transformer-Based Hierarchical Attention Network. Comput Intell Neurosci, 2022, 2022: 4207940.
25. Barboi C, Tzavelis A, Muhammad LN. Comparison of Severity of Illness Scores and Artificial Intelligence Models that are predictive of intensive care unit mortality: meta-analysis and review of the literature. JMIR Med Inform, 2022, 10(5): e35293.
26. Hong N, Liu C, Gao JW, et al. State of the art of machine learning-enabled clinical decision support in intensive care units: literature review. JMIR Med Inform, 2022 , 10(3): e28781.
27. Srivani M, Murugappan A, Mala T. Cognitive computing technological trends and future research directions in healthcare - a systematic literature review. Artif Intell Med, 2023, 138: 102513.
28. Zeguendry A, Jarir Z, Quafafou M. Quantum machine learning: a review and case studies. Entropy (Basel), 2023, 25(2): 287.

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機器學習在膿毒癥及危重癥患者預后評估中的應用

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