|
|
Research progress in constructing postoperative delirium risk prediction model based on machine learning algorithm |
GAO Shengrun, LI Yun, LI Yuheng, et al |
|
|
|
Received: 11 September 2023
|
|
|
|
|
[4] |
Choi R Y,Coyner A S,Kalpathy-Cramer J,et al.Introduction to machine learning, neural networks,and deep learning[J].Transl Vis Sci Technol,2020,9(2):14.
|
[1] |
Shaji P,McCabe C.A narrative review of preventive measures for postoperative delirium in older adults[J].Br J Nurs,2021,30(6):367-373.
|
[5] |
路 薇,孙东旭,高景宏,等.面向精准医疗的大数据分析与建模关键技术综述[J].中国医院管理,2021,41(5):19-25.
|
[2] |
Duning T, Ilting R K, Beckhuis M, et al. Postoperative delirium-treatment and prevention[J].Curr Opin Anaesthesiol,2021,34(1):27-32.
|
[6] |
贾玉龙,周 洁,陈 颖,等.临床预测模型的综合评价体系研究[J].中国卫生统计,2019,36(5):728-730,734.
|
[3] |
廖华龙,曾小茜,李华凤,等.机器学习在疾病预测中的应用[J].生物医学工程研究,2021,40(2):203-209.
|
[7] |
谷鸿秋,周支瑞,章仲恒,等.临床预测模型:基本概念、应用场景及研究思路[J].中国循证心血管医学杂志,2018,10(12):1454-1456.
|
[8] |
Hua Y,Chen S,Xiong X,et al.Risk factors for postoperative delirium in elderly urological patients:a meta-analysis[J].Medicine (Baltimore),2022,101(38):e30696.
|
[4] |
Choi R Y,Coyner A S,Kalpathy-Cramer J,et al.Introduction to machine learning, neural networks,and deep learning[J].Transl Vis Sci Technol,2020,9(2):14.
|
[9] |
Zhang M,Zhang X,Gao L,et al.Incidence, predictors and health outcomes of delirium in very old hospitalized patients: a prospective cohort study[J].BMC Geriatr,2022,22(1):262.
|
[5] |
路 薇,孙东旭,高景宏,等.面向精准医疗的大数据分析与建模关键技术综述[J].中国医院管理,2021,41(5):19-25.
|
[10] |
田 甜,景 慧,付 佳.恶性肿瘤患者谵妄发生风险的预测模型研究[J].实用医学杂志,2021,37(20):2641-2646.
|
[6] |
贾玉龙,周 洁,陈 颖,等.临床预测模型的综合评价体系研究[J].中国卫生统计,2019,36(5):728-730,734.
|
[7] |
谷鸿秋,周支瑞,章仲恒,等.临床预测模型:基本概念、应用场景及研究思路[J].中国循证心血管医学杂志,2018,10(12):1454-1456.
|
[11] |
Arita A,Takahashi H,Ogino T,et al.Grip strength as a predictor of postoperative delirium in patients with colorectal cancers[J].Ann Gastroenterol Surg, 2021,6(2):265-272.
|
[8] |
Hua Y,Chen S,Xiong X,et al.Risk factors for postoperative delirium in elderly urological patients:a meta-analysis[J].Medicine (Baltimore),2022,101(38):e30696.
|
[12] |
Onuma H,InoseH,Yoshii T,et al.Preoperative risk factors for delirium in patients aged≥75 years undergoing spinal surgery:a retrospective study[J].J Int Med Res,2020,48(10):30.
|
[9] |
Zhang M,Zhang X,Gao L,et al.Incidence, predictors and health outcomes of delirium in very old hospitalized patients: a prospective cohort study[J].BMC Geriatr,2022,22(1):262.
|
[13] |
Reisinger M,Reininghaus E Z,Biasi J,et al.Delirium-associated medication in people at risk:a systematic update review,meta-analyses,andgrade-profiles[J].Acta Psychiatr Scand,2023,147(1):16-42.
|
[10] |
田 甜,景 慧,付 佳.恶性肿瘤患者谵妄发生风险的预测模型研究[J].实用医学杂志,2021,37(20):2641-2646.
|
[14] |
王琦琦,于石成,亓 晓,等.Logistic族回归及其应用[J].中华预防医学杂志,2019,53(9):955-960.
|
[15] |
王玉伟,李 慧.构建及验证基于Logistic回归的Stanford A型主动脉夹层术后谵妄风险预测模型效果[J].临床研究,2023,31(2):7-11.
|
[11] |
Arita A,Takahashi H,Ogino T,et al.Grip strength as a predictor of postoperative delirium in patients with colorectal cancers[J].Ann Gastroenterol Surg, 2021,6(2):265-272.
|
[16] |
陶立元,张 华,赵一鸣.列线图的制作要点及其应用[J].中华儿科杂志,2017,55(5):323.
|
[12] |
Onuma H,InoseH,Yoshii T,et al.Preoperative risk factors for delirium in patients aged≥75 years undergoing spinal surgery:a retrospective study[J].J Int Med Res,2020,48(10):30.
|
[17] |
李 繁,黎仕焕,谢 爽.老年患者肺癌根治术后谵妄的危险因素及列线图预测模型的建立[J].临床麻醉学杂志,2022,38(10):1013-1019.
|
[13] |
Reisinger M,Reininghaus E Z,Biasi J,et al.Delirium-associated medication in people at risk:a systematic update review,meta-analyses,andgrade-profiles[J].Acta Psychiatr Scand,2023,147(1):16-42.
|
[18] |
Li B,Ju J,Zhao J,et al.A nomogram to predict delirium after hip replacement in elderly patients with femoral neck fractures[J].Orthop Surg,2022,14(12):3195-3200.
|
[14] |
王琦琦,于石成,亓 晓,等.Logistic族回归及其应用[J].中华预防医学杂志,2019,53(9):955-960.
|
[19] |
Chen J,Ji X,Xing H.Risk factors and a nomogram model for postoperative delirium in elderly gastric cancer patients after laparoscopic gastrectomy[J].World J Surg Oncol,2022,20(1):319.
|
[15] |
王玉伟,李 慧.构建及验证基于Logistic回归的Stanford A型主动脉夹层术后谵妄风险预测模型效果[J].临床研究,2023,31(2):7-11.
|
[20] |
Malloy E J,Spiegelman D,Eisen E A.Comparing measures of model selection for penalized splines in Cox models[J].Comput Stat Data Anal,2009,53(7):2605-2616.
|
[16] |
陶立元,张 华,赵一鸣.列线图的制作要点及其应用[J].中华儿科杂志,2017,55(5):323.
|
[21] |
黄宛冰,张玉芬,吴前胜,等.基于Cox回归的Stanford B型主动脉夹层术后谵妄预测模型的构建[J].护理学杂志,2023,38(3):27-31.
|
[17] |
李 繁,黎仕焕,谢 爽.老年患者肺癌根治术后谵妄的危险因素及列线图预测模型的建立[J].临床麻醉学杂志,2022,38(10):1013-1019.
|
[22] |
汪靖翔.决策树算法的原理研究和实际应用[J].电脑编程技巧与维护,2022(8):54-56,72.
|
[18] |
Li B,Ju J,Zhao J,et al.A nomogram to predict delirium after hip replacement in elderly patients with femoral neck fractures[J].Orthop Surg,2022,14(12):3195-3200.
|
[23] |
廖华龙,曾小茜,李华凤,等.机器学习在疾病预测中的应用[J].生物医学工程研究,2021,40(2):203-209.
|
[19] |
Chen J,Ji X,Xing H.Risk factors and a nomogram model for postoperative delirium in elderly gastric cancer patients after laparoscopic gastrectomy[J].World J Surg Oncol,2022,20(1):319.
|
[24] |
Liu Y,Shen W,Tian Z.Usingmachine learning algorithms to predict high-risk factors for postoperative delirium in elderly patients[J].Clin Interv Aging,2023,18:157-168.
|
[20] |
Malloy E J,Spiegelman D,Eisen E A.Comparing measures of model selection for penalized splines in Cox models[J].Comput Stat Data Anal,2009,53(7):2605-2616.
|
[25] |
Röhr V,Blankertz B,Radtke F M,et al.Machine-learning model predicting postoperative delirium in older patients using intraoperative frontal electroencephalographic signatures[J].Front Aging Neurosci,2022,14:911088.
|
[21] |
黄宛冰,张玉芬,吴前胜,等.基于Cox回归的Stanford B型主动脉夹层术后谵妄预测模型的构建[J].护理学杂志,2023,38(3):27-31.
|
[26] |
Bishara A,Chiu C,Whitlock E L,et al.Postoperative delirium prediction using machine learning models and preoperative electronic health record data[J].BMC Anesthesiol,2022,22(1):8.
|
[22] |
汪靖翔.决策树算法的原理研究和实际应用[J].电脑编程技巧与维护,2022(8):54-56,72.
|
[23] |
廖华龙,曾小茜,李华凤,等.机器学习在疾病预测中的应用[J].生物医学工程研究,2021,40(2):203-209.
|
[27] |
Jung J W,Hwang S,Ko S,et al.A machine-learning model to predict postoperative delirium following knee arthroplasty using electronic health records[J].BMC Psychiatry,2022, 22(1):436.
|
[24] |
Liu Y,Shen W,Tian Z.Usingmachine learning algorithms to predict high-risk factors for postoperative delirium in elderly patients[J].Clin Interv Aging,2023,18:157-168.
|
[28] |
Wang Y,Lei L,Ji M,et al.Predicting postoperative delirium after microvascular decompression surgery with machine learning[J].J Clin Anesth,2020, 66:109.
|
[25] |
Röhr V,Blankertz B,Radtke F M,et al.Machine-learning model predicting postoperative delirium in older patients using intraoperative frontal electroencephalographic signatures[J].Front Aging Neurosci,2022,14:911088.
|
[29] |
Hu X Y,Liu H,Zhao X,et al.Automated machine learning-based model predicts postoperative delirium using readily extractable perioperative collected electronic data[J].CNS Neurosci Ther,2022,28(4):608-618.
|
[26] |
Bishara A,Chiu C,Whitlock E L,et al.Postoperative delirium prediction using machine learning models and preoperative electronic health record data[J].BMC Anesthesiol,2022,22(1):8.
|
[30] |
Moon K J,Son C S,Lee J H,et al.The development of a web-based app employing machine learning for delirium prevention in long-term care facilities in South Korea[J].BMC Med Inform Decis Mak,2022,22(1):220.
|
[27] |
Jung J W,Hwang S,Ko S,et al.A machine-learning model to predict postoperative delirium following knee arthroplasty using electronic health records[J].BMC Psychiatry,2022, 22(1):436.
|
[28] |
Wang Y,Lei L,Ji M,et al.Predicting postoperative delirium after microvascular decompression surgery with machine learning[J].J Clin Anesth,2020, 66:109.
|
[29] |
Hu X Y,Liu H,Zhao X,et al.Automated machine learning-based model predicts postoperative delirium using readily extractable perioperative collected electronic data[J].CNS Neurosci Ther,2022,28(4):608-618.
|
[30] |
Moon K J,Son C S,Lee J H,et al.The development of a web-based app employing machine learning for delirium prevention in long-term care facilities in South Korea[J].BMC Med Inform Decis Mak,2022,22(1):220.
|
[1] |
. [J]. Med. J. Chin. Peop. Armed Poli. Forc., 2024, 35(5): 439-442. |
|
|
|
|