北京大学学报(医学版) ›› 2022, Vol. 54 ›› Issue (3): 458-467. doi: 10.19723/j.issn.1671-167X.2022.03.010
邓宇含1,姜勇2,3,王子尧1,刘爽1,汪雨欣1,刘宝花1,*()
Yu-han DENG1,Yong JIANG2,3,Zi-yao WANG1,Shuang LIU1,Yu-xin WANG1,Bao-hua LIU1,*()
摘要:
目的: 基于引入注意力机制的长短期记忆网络(long short-term memory,LSTM)和L1正则化的Logistic回归筛选变量,再通过传统的Logistic回归建立重症监护病房(intensive care unit,ICU)脑卒中患者院内死亡风险预测模型并评价模型效果。方法: 选取重症医学信息数据库(Medical Information Mart for Intensive Care-Ⅳ,MIMIC-Ⅳ)中的脑卒中患者作为研究对象,以是否发生院内死亡作为结局变量,备选预测因子包括人口学特征、合并症、入院48 h内实验室检查和生命体征检查等。将数据根据结局指标以8 ∶2的比例随机进行10次训练集和测试集的划分,在训练集上构建LSTM和L1正则化的Logistic回归模型,在测试集上选取重要程度排名前10的变量的并集纳入Logistic回归建立预测模型,以受试者工作特征曲线下面积(area under curve, AUC)、灵敏度、特异度、预测准确度为指标对模型进行评价,并与未预先进行变量筛选的前进法Logistic回归模型的预测效果进行比较。结果: 共纳入2 755例脑卒中患者的2 979条ICU入院记录,其中院内死亡记录占17.66%。两个变量筛选模型中,L1正则化的Logistic回归模型的AUC显著优于LSTM模型(0.819±0.031 vs. 0.760±0.018, P < 0.001),两个模型中重要程度均位于前10的变量包括年龄、血糖和尿素氮。最终预测模型的AUC为0.85,灵敏度为85.98%,特异度为71.74%,预测准确率为74.26%,优于未预先进行变量筛选的前进法Logistic回归模型。结论: 用引入注意力机制的LSTM和L1正则的Logistic回归筛选出的变量的预测效果较好,具有一定的临床价值。
中图分类号:
1 |
Katan M , Luft A . Global burden of stroke[J]. Semin Neurol, 2018, 38 (2): 208- 211.
doi: 10.1055/s-0038-1649503 |
2 |
Rochmah TN , Rahmawati IT , Dahlui M , et al. Economic burden of stroke disease: A systematic review[J]. Int J Environ Res Public Health, 2021, 18 (14): 7552.
doi: 10.3390/ijerph18147552 |
3 |
Sarti C , Rastenyte D , Cepaitis Z , et al. International trends in mortality from stroke, 1968 to 1994[J]. Stroke, 2000, 31 (7): 1588- 1601.
doi: 10.1161/01.STR.31.7.1588 |
4 |
Handschu R , Haslbeck M , Hartmann A , et al. Mortality prediction in critical care for acute stroke: Severity of illness-score or coma-scale?[J]. J Neurol, 2005, 252 (10): 1249- 1254.
doi: 10.1007/s00415-005-0853-5 |
5 |
Ryan L , Lam C , Mataraso S , et al. Mortality prediction model for the triage of COVID-19, pneumonia, and mechanically ventilated ICU patients: A retrospective study[J]. Ann Med Surg (Lond), 2020, 59, 207- 216.
doi: 10.1016/j.amsu.2020.09.044 |
6 |
Nemati S , Holder A , Razmi F , et al. An interpretable machine learning model for accurate prediction of sepsis in the ICU[J]. Crit Care Med, 2018, 46 (4): 547- 553.
doi: 10.1097/CCM.0000000000002936 |
7 |
LeCun Y , Bengio Y , Hinton G . Deep learning[J]. Nature, 2015, 521 (7553): 436- 444.
doi: 10.1038/nature14539 |
8 |
Cheng JZ , Ni D , Chou YH , et al. Computer-aided diagnosis with deep learning architecture: Applications to breast lesions in US images and pulmonary nodules in CT scans[J]. Sci Rep, 2016, 6, 24454.
doi: 10.1038/srep24454 |
9 |
Kooi T , Litjens G , van Ginneken B , et al. Large scale deep learning for computer aided detection of mammographic lesions[J]. Med Image Anal, 2017, 35, 303- 312.
doi: 10.1016/j.media.2016.07.007 |
10 | Choi E , Bahadori MT , Schuetz A , et al. Doctor AI: Predicting clinical events via recurrent neural networks[J]. JMLR Workshop Conf Proc, 2016, 56, 301- 318. |
11 |
Hochreiter S , Schmidhuber J . Long short-term memory[J]. Neural Comput, 1997, 9 (8): 1735- 1780.
doi: 10.1162/neco.1997.9.8.1735 |
12 |
Thorsen-Meyer HC , Nielsen AB , Nielsen AP , et al. Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: A retrospective study of high-frequency data in electronic patient records[J]. Lancet Digit Health, 2020, 2 (4): e179- e191.
doi: 10.1016/S2589-7500(20)30018-2 |
13 | Xia J , Pan S , Zhu M , et al. A long short-term memory ensemble approach for improving the outcome prediction in intensive care unit[J]. Comput Math Methods Med, 2019, 2019, 8152713. |
14 |
Maheshwari S , Agarwal A , Shukla A , et al. A comprehensive evaluation for the prediction of mortality in intensive care units with LSTM networks: Patients with cardiovascular disease[J]. Biomed Tech (Berl), 2020, 65 (4): 435- 446.
doi: 10.1515/bmt-2018-0206 |
15 |
Ho LV , Aczon M , Ledbetter D , et al. Interpreting a recurrent neural network's predictions of ICU mortality risk[J]. J Biomed Inform, 2021, 114, 103672.
doi: 10.1016/j.jbi.2021.103672 |
16 | 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. |
17 |
Jhou HJ , Chen PH , Yang LY , et al. Plasma anion gap and risk of in-hospital mortality in patients with acute ischemic stroke: Analysis from the MIMIC-Ⅳ database[J]. J Pers Med, 2021, 11 (10): 1004.
doi: 10.3390/jpm11101004 |
18 |
Zhao N , Hu W , Wu Z , et al. The red blood cell distribution width-albumin ratio: A promising predictor of mortality in stroke patients[J]. Int J Gen Med, 2021, 14, 3737- 3747.
doi: 10.2147/IJGM.S322441 |
19 | 邱锡鹏. 神经网络与深度学习[M]. 北京: 机械工业出版社, 2020: 141- 145. |
20 |
Kaji DA , Zech JR , Kim JS , et al. An attention based deep lear-ning model of clinical events in the intensive care unit[J]. PLoS One, 2019, 14 (2): e0211057.
doi: 10.1371/journal.pone.0211057 |
21 |
Lopez Bernal J , Soumerai S , Gasparrini A . A methodological framework for model selection in interrupted time series studies[J]. J Clin Epidemiol, 2018, 103, 82- 91.
doi: 10.1016/j.jclinepi.2018.05.026 |
22 |
Yu Y , Si X , Hu C , et al. A review of recurrent neural networks: LSTM cells and network architectures[J]. Neural Comput, 2019, 31 (7): 1235- 1270.
doi: 10.1162/neco_a_01199 |
23 |
Gandin I , Scagnetto A , Romani S , et al. Interpretability of time-series deep learning models: A study in cardiovascular patients admitted to intensive care unit[J]. J Biomed Inform, 2021, 121, 103876.
doi: 10.1016/j.jbi.2021.103876 |
24 |
Weimar C , Ziegler A , Konig IR , et al. Predicting functional outcome and survival after acute ischemic stroke[J]. J Neurol, 2002, 249 (7): 888- 895.
doi: 10.1007/s00415-002-0755-8 |
25 | Koyama T , Uchiyama Y , Domen K . Outcome in stroke patients is associated with age and fractional anisotropy in the cerebral peduncles: A multivariate regression study[J]. Prog Rehabil Med, 2020, 5, 20200006. |
26 |
Duarte E , Marco E , Muniesa JM , et al. Early detection of non-ambulatory survivors six months after stroke[J]. NeuroRehabilitation, 2010, 26 (4): 317- 323.
doi: 10.3233/NRE-2010-0568 |
27 |
Fuentes B , Castillo J , San Jose B , et al. The prognostic value of capillary glucose levels in acute stroke[J]. Stroke, 2009, 40 (2): 562- 568.
doi: 10.1161/STROKEAHA.108.519926 |
28 |
Baird TA , Parsons MW , Phanh T , et al. Persistent poststroke hyperglycemia is independently associated with infarct expansion and worse clinical outcome[J]. Stroke, 2003, 34 (9): 2208- 2214.
doi: 10.1161/01.STR.0000085087.41330.FF |
29 |
Förstermann U , Münzel T . Endothelial nitric oxide synthase in vascular disease: From marvel to menace[J]. Circulation, 2006, 113 (13): 1708- 1714.
doi: 10.1161/CIRCULATIONAHA.105.602532 |
30 |
Virley D , Hadingham SJ , Roberts JC , et al. A new primate model of focal stroke: Endothelin-1-induced middle cerebral artery occlusion and reperfusion in the common marmoset[J]. J Cereb Blood Flow Metab, 2004, 24 (1): 24- 41.
doi: 10.1097/01.WCB.0000095801.98378.4A |
31 |
Martini SR , Kent TA . Hyperglycemia in acute ischemic stroke: A vascular perspective[J]. J Cereb Blood Flow Metab, 2007, 27 (3): 435- 451.
doi: 10.1038/sj.jcbfm.9600355 |
32 |
You S , Zheng D , Zhong C , et al. Prognostic significance of blood urea nitrogen in acute ischemic stroke[J]. Circ J, 2018, 82 (2): 572- 578.
doi: 10.1253/circj.CJ-17-0485 |
33 |
Cheng J , Sun J , Yao K , et al. A variable selection method based on mutual information and variance inflation factor[J]. Spectrochim Acta A Mol Biomol Spectrosc, 2022, 268, 120652.
doi: 10.1016/j.saa.2021.120652 |
34 | Ge W , Huh JW , Park YR , et al. An interpretable ICU mortality prediction model based on Logistic regression and recurrent neural networks with LSTM units[J]. AMIA Annu Symp Proc, 2018, 2018, 460- 469. |
35 |
Koppe G , Meyer-Lindenberg A , Durstewitz D . Deep learning for small and big data in psychiatry[J]. Neuropsychopharmacology, 2021, 46 (1): 176- 190.
doi: 10.1038/s41386-020-0767-z |
[1] | 杨若彤,王梦莹,李春男,于欢,王小文,吴俊慧,王斯悦,王伽婷,陈大方,吴涛,胡永华. 缺血性脑卒中全基因组关联研究提示阳性基因位点与睡眠行为的交互作用[J]. 北京大学学报(医学版), 2022, 54(3): 412-420. |
[2] | 吴俊慧,武轶群,吴瑶,王紫荆,吴涛,秦雪英,王梦莹,王小文,王伽婷,胡永华. 北京城镇职工2型糖尿病患者缺血性脑卒中发病率及主要危险因素[J]. 北京大学学报(医学版), 2022, 54(2): 249-254. |
[3] | 蓝璘,贺洋,安金刚,张益. 颧骨缺损不同修复重建方法和预后的回顾性分析[J]. 北京大学学报(医学版), 2022, 54(2): 356-362. |
[4] | 袁临天,马利沙,刘润园,齐伟,张栌丹,王贵燕,王宇光. 计算机模拟亚甲基蓝与牙龈卟啉单胞菌部分蛋白的分子对接[J]. 北京大学学报(医学版), 2022, 54(1): 23-30. |
[5] | 任国勇,吴雪梅,李颖,李婕妤,孙伟平,黄一宁. 大血管闭塞性脑卒中亚急性期磁敏感血管征的表现[J]. 北京大学学报(医学版), 2021, 53(6): 1133-1138. |
[6] | 王飞,朱翔,贺蓓,朱红,沈宁. 自发缓解的滤泡性细支气管炎伴非特异性间质性肺炎1例报道并文献复习[J]. 北京大学学报(医学版), 2021, 53(6): 1196-1200. |
[7] | 高伟波,石茂静,张海燕,吴春波,朱继红. 显著高铁蛋白血症与噬血细胞性淋巴组织细胞增多症的相互关系[J]. 北京大学学报(医学版), 2021, 53(5): 921-927. |
[8] | 张梅香,史文芝,刘建新,王春键,李燕,王蔚,江滨. MLL-AF6融合基因阳性急性髓系白血病的临床特征及预后[J]. 北京大学学报(医学版), 2021, 53(5): 915-920. |
[9] | 蒋艳芳,王健,王永健,刘佳,裴殷,刘晓鹏,敖英芳,马勇. 前交叉韧带翻修重建术后中长期临床疗效及影响因素[J]. 北京大学学报(医学版), 2021, 53(5): 857-863. |
[10] | 肖若陶,刘承,徐楚潇,何为,马潞林. 术前血小板参数与局部进展期肾细胞癌预后[J]. 北京大学学报(医学版), 2021, 53(4): 647-652. |
[11] | 于妍斐,何世明,吴宇财,熊盛炜,沈棋,李妍妍,杨风,何群,李学松. 延胡索酸水合酶缺陷型肾细胞癌的临床病理特征及预后[J]. 北京大学学报(医学版), 2021, 53(4): 640-646. |
[12] | 赵勋,颜野,黄晓娟,董靖晗,刘茁,张洪宪,刘承,马潞林. 癌栓粘连血管壁对非转移性肾细胞癌合并下腔静脉癌栓患者手术及预后的影响[J]. 北京大学学报(医学版), 2021, 53(4): 665-670. |
[13] | 陈怀安,刘硕,李秀君,王哲,张潮,李凤岐,苗文隆. 炎症生物标志物对输尿管尿路上皮癌患者预后预测的临床价值[J]. 北京大学学报(医学版), 2021, 53(2): 302-307. |
[14] | 刘世博,高辉,冯元春,李静,张彤,万利,刘燕鹰,李胜光,罗成华,张学武. 腹膜后纤维化致肾盂积水的临床分析:附17例报道[J]. 北京大学学报(医学版), 2020, 52(6): 1069-1074. |
[15] | 候越,赵旭彤,谢志颖,袁云,王朝霞. 线粒体DNA 8344 A>G突变导致的MELAS/MERRF/Leigh重叠综合征[J]. 北京大学学报(医学版), 2020, 52(5): 851-855. |
|