Citation: | GONG Huanhuan, KE Xiaowei, WANG Aimin, LI Xiangmin. An Interpretable Machine Learning Model for Predicting In-hospital Death Risk in Patients with Cardiac Arrest: Based on US Medical Information Mart for Intensive Care Database Ⅳ 2.0[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(3): 528-535. DOI: 10.12290/xhyxzz.2022-0733 |
[1] |
陈红, 张重阳, 徐俊祥. 急诊院前、院内心脏骤停患者心肺复苏效果分析[J]. 河北医药, 2017, 39: 2475-2477. DOI: 10.3969/j.issn.1002-7386.2017.16.019
|
[2] |
Chen J, Mei Z, Wang Y, et al. A nomogram to predict in hospital mortality in post-cardiac arrest patients: a retrospective cohort study[J]. Pol Arch Intern Med, 2023, 133: 16325
|
[3] |
Ngiam KY, Khor IW. Big data and machine learning algorithms for health-care delivery[J]. Lancet Oncol, 2019, 20: e262-e273. DOI: 10.1016/S1470-2045(19)30149-4
|
[4] |
Rauschert S, Raubenheimer K, Melton PE, et al. Machine learning and clinical epigenetics: a review of challenges for diagnosis and classification[J]. Clin Epigenetics, 2020, 12: 51. DOI: 10.1186/s13148-020-00842-4
|
[5] |
Lee YW, Choi JW, Shin EH. Machine learning model for predicting malaria using clinical information[J]. Comput Biol Med, 2021, 129: 104151. DOI: 10.1016/j.compbiomed.2020.104151
|
[6] |
Hou N, Li M, He L, et al. Predicting 30-days mortality for MIMIC-Ⅲ patients with sepsis-3: a machine learning approach using XGboost[J]. J Transl Med, 2020, 18: 462. DOI: 10.1186/s12967-020-02620-5
|
[7] |
Chu J, Leung KHB, Snobelen P, et al. Machine learning-based dispatch of drone-delivered defibrillators for out-of-hospital cardiac arrest[J]. Resuscitation, 2021, 162: 120-127. DOI: 10.1016/j.resuscitation.2021.02.028
|
[8] |
苏枫, 张少衡, 陈楠楠, 等. 基于机器学习分类判断算法构建心力衰竭疾病分期模型[J]. 中国组织工程研究, 2014, 49: 7938-7942. DOI: 10.3969/j.issn.2095-4344.2014.49.012
|
[9] |
张颖莹, 刘怡果, 赵丹, 等. 基于机器学习建立脓毒症心肾综合征患者早期死亡风险预测模型[J]. 中华肾脏病杂志, 2022, 38: 785-793. DOI: 10.3760/cma.j.cn441217-20211126-00113
|
[10] |
Stevens RD. Machine Learning to Decode the Electroencephalography for Post Cardiac Arrest Neuroprognostication[J]. Crit Care Med, 2019, 47: 1474-1476. DOI: 10.1097/CCM.0000000000003932
|
[11] |
Jennings JB. Can machine learning predict recurrent cardiac arrest?[J]. Resuscitation, 2023, 184: 109704. DOI: 10.1016/j.resuscitation.2023.109704
|
[12] |
Blomberg SN, Folke F, Ersbøll AK, et al. Machine learning as a supportive tool to recognize cardiac arrest in emergency calls[J]. Resuscitation, 2019, 138: 322-329. DOI: 10.1016/j.resuscitation.2019.01.015
|
[13] |
Kwon JM, Jeon Kh, Kim HM, et al. Deep-learning-based out-of-hospital cardiac arrest prognostic system to predict clinical outcomes[J]. Resuscitation, 2019, 139: 84-91. DOI: 10.1016/j.resuscitation.2019.04.007
|
[14] |
Lundberg SM, Erion G, Chen H, et al. From Local Explanations to Global Understanding with Explainable AI for Trees[J]. Nat Mach Intell, 2020, 2: 56-67. DOI: 10.1038/s42256-019-0138-9
|
[15] |
王鑫, 廖彬, 李敏, 等. 融合LightGBM与SHAP的糖尿病预测及其特征分析方法[J]. 小型微型计算机系统, 2022, 43: 1877-1885. https://www.cnki.com.cn/Article/CJFDTOTAL-XXWX202209012.htm
|
[16] |
Wu TT, Lin XQ, Mu Y, et al. Machine learning for early prediction of in-hospital cardiac arrest in patients with acute coronary syndromes[J]. Clin Cardiol, 2021, 44: 349-356. DOI: 10.1002/clc.23541
|
[17] |
郑萍, 刘宁. 机器学习应用于院外心脏骤停神经系统预后预测模型的系统评价[J]. 中国胸心血管外科临床杂志, 2022, 29: 1172-1180. https://www.cnki.com.cn/Article/CJFDTOTAL-ZXYX202209013.htm
|
[18] |
吴秋硕, 陆宗庆, 刘瑜, 等. 机器学习应用于心脏骤停早期预测模型的系统评价[J]. 中国循证医学杂志, 2021, 21: 942-952. https://www.cnki.com.cn/Article/CJFDTOTAL-ZZXZ202108011.htm
|
[19] |
Mayampurath A, Hagopian R, Venable L, et al. Comparison of Machine Learning Methods for Predicting Outcomes After In-Hospital Cardiac Arrest[J]. Crit Care Med, 2022, 50: e162-e172. DOI: 10.1097/CCM.0000000000005286
|
[20] |
Yan J, Xu Y, Cheng Q, et al. LightGBM: accelerated genomically designed crop breeding through ensemble learning[J]. Genome Biol, 2021, 22: 271. DOI: 10.1186/s13059-021-02492-y
|
[21] |
张红莉, 李月琴, 韩磊, 等. 基于LGBM和深度神经网络的HRRP目标识别方法[J]. 探测与控制学报, 2022, 44: 97-103, 114. https://www.cnki.com.cn/Article/CJFDTOTAL-XDYX202202017.htm
|
[22] |
Rufo DD, Debelee TG, Ibenthal A, et al. Diagnosis of Diabetes Mellitus Using Gradient Boosting Machine (LightGBM)[J]. Diagnostics (Basel), 2021, 11: 1714. DOI: 10.3390/diagnostics11091714
|
[23] |
Ge C, Deng F, Chen W, et al. Machine learning for early prediction of sepsis-associated acute brain injury[J]. Front Med (Lausanne), 2022, 9: 962027. http://pubmed.ncbi.nlm.nih.gov/35562803/
|
[24] |
Nadolny K, Bujak K, Obremska M, et al. Glasgow Coma Scale score of more than four on admission predicts in-hospital survival in patients after out-of-hospital cardiac arrest[J]. Am J Emerg Med, 2021, 42: 90-94.
|
[25] |
蔡兰兰, 杨增强. 70例心脏骤停后复苏患者格拉斯哥昏迷评分及预后分析[J]. 内科急危重症杂志, 2018, 24: 431-433. https://www.cnki.com.cn/Article/CJFDTOTAL-NKJW201805028.htm
|
[26] |
Chen YC, Hung MS, Liu CY, et al. The association of emergency department administration of sodium bicarbonate after out of hospital cardiac arrest with outcomes[J]. Am J Emerg Med, 2018, 36: 1998-2004. http://pubmed.ncbi.nlm.nih.gov/29534919/
|
[27] |
Celik T, Ozturk C, Balta S, et al. Sodium bicarbonate dilemma in patients with out-of-hospital cardiac arrest: A double-edged sword[J]. Am J Emerg Med, 2016, 34: 1314-1315. http://www.onacademic.com/detail/journal_1000038851198510_6788.html
|
[28] |
Kim HJ, Park KN, Kim SH, et al. Association between the neutrophil-to-lymphocyte ratio and neurological outcomes in patients undergoing targeted temperature management after cardiac arrest[J]. J Crit Care, 2018, 47: 227-231. http://www.sciencedirect.com/science/article/pii/S0883944118303708
|
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