1 |
马林,巢宝华,曹雷,等. 2007-2017年中国脑卒中流行趋势及特征分析[J/CD].中华脑血管病杂志(电子版),2020,14(5):253-258.
|
2 |
王瑶,张劲松,姜丽丽,等.急性缺血性脑卒中合并上肢动脉栓塞三例救治分析[J/CD].中华危重症医学杂志(电子版),2022,15(5):399-404.
|
3 |
郭芝廷,金静芬,刘苑菲,等.院前流程优化对缺血性脑卒中患者干预效果的Meta分析[J].中华急危重症护理杂志,2023,4(7):644-653.
|
4 |
《中国脑卒中防治报告2021》编写组.《中国脑卒中防治报告2021》概要[J].中国脑血管病杂志,2023,20(11):783-792,封3.
|
5 |
Adebayo OD, Culpan G. Diagnostic accuracy of computed tomography perfusion in the prediction of haemorrhagic transformation and patient outcome in acute ischaemic stroke: a systematic review and meta-analysis[J]. Eur Stroke J, 2020, 5 (1): 4-16.
|
6 |
谭颗,钱德才,邓磊.静脉溶栓桥接血管内治疗急性缺血性脑卒中患者41例分析[J/CD].中华危重症医学杂志(电子版),2019,12(6):409-412.
|
7 |
Yaghi S, Boehme AK, Dibu J, et al. Treatment and outcome of thrombolysis-related hemorrhage: a multicenter retrospective study[J]. JAMA Neurol, 2015, 72 (12): 1451-1457.
|
8 |
崔颖,佟旭,王伊龙,等.急性缺血性卒中患者阿替普酶静脉溶栓后发生早期神经功能恶化的危险因素分析[J].中华神经科杂志,2016,49(12):925-931.
|
9 |
Chen Y, Lin Z, Zhao X, et al. Deep learning-based classification of hyperspectral data[J]. IEEE J Sel Top Appl Earth Obs Remote Sens, 2014, 7 (6): 2094-2107.
|
10 |
杨童亮,胡东,唐超,等.基于SMA-VMD-GRU模型的变压器油中溶解气体含量预测[J].电工技术学报,2023,38(1):117-130.
|
11 |
Hacke W, Kaste M, Fieschi C, et al. Randomised double-blind placebo-controlled trial of thrombolytic therapy with intravenous alteplase in acute ischaemic stroke (ECASS II). Second European-Australasian Acute Stroke Study Investigators[J]. Lancet, 1998, 352 (9136): 1245-1251.
|
12 |
郭晓,刘颖,王蕊,等.人工智能辅助系统在宫颈病变细胞学诊断中的应用效果研究[J].癌变·畸变·突变,2022,34(5):361-365.
|
13 |
方国旭,郭鹏飞,范鉴慧,等.基于可解释人工智能的临床决策支持系统:孟超肝病外脑[J].中华消化外科杂志,2023,22(1):70-80.
|
14 |
魏来,谭文莉,詹松华.脑梗死影像智能诊断及预测的研究进展[J].中国中西医结合影像学杂志,2023,21(1):74-78.
|
15 |
Krizhevsky A, Sutskever L, Hinton G. ImageNet classification with deep convolutional neural networks[J]. Adv Neural Inf Process Syst, 2012, 25 (2): 84-90.
|
16 |
Soltanpour M, Greiner R, Boulanger P, et al. Improvement of automatic ischemic stroke lesion segmentation in CT perfusion maps using a learned deep neural network[J]. Comput Biol Med, 2021 (137): 104849.
|
17 |
Zhao X, Shi X, Zhang S. Facial expression recognition via deep learning[J]. IETE Tech Rev, 2015, 32 (5): 347-355.
|
18 |
Schmidhuber J. Deep learning in neural networks: an overview[J]. Neural Netw, 2015 (61): 85-117.
|
19 |
郑飞,陈绪珠.深度学习在脑胶质母细胞瘤的研究进展[J].磁共振成像,2022,13(3):115-117.
|
20 |
张恒,张赛,孙佳伟,等.深度学习脑肿瘤MRI图像分类研究进展[J].磁共振成像,2023,14(1):166-171,193.
|
21 |
赵小明,杨轶娇,张石清.面向深度学习的多模态情感识别研究进展[J].计算机科学与探索,2022,16(7):1479-1503.
|
22 |
付占威,蔡正昊,李树春,等.基于磁共振成像检查的集成深度学习模型预测中低位直肠癌切除术中直线切割闭合器使用次数的临床价值[J].中华消化外科杂志,2023,22(9:1129-1138.
|
23 |
Mainali S, Darsie ME, Smetana KS. Machine learning in action: stroke diagnosis and outcome prediction[J]. Front Neurol, 2021 (12): 734345.
|
24 |
Chung CC, Chan L, Bamodu OA, et al. Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death[J]. Sci Rep, 2020, 10 (1): 20501.
|
25 |
Wang F, Huang Y, Xia Y, et al. Personalized risk prediction of symptomatic intracerebral hemorrhage after stroke thrombolysis using a machine-learning model[J]. Ther Adv Neurol Disord, 2020 (13): 1756286420902358.
|
26 |
Choi JM, Seo SY, Kim PJ, et al. Prediction of hemorrhagic transformation after ischemic stroke using machine learning[J]. J Pers Med, 2021, 11 (9): 863.
|
27 |
王海东,张璐,王洁,等. C5.0决策树与RBF神经网络模型用于急性缺血性脑卒中出血性转化的风险预测性能比较[J].中华疾病控制杂志,2019,23(2):228-233.
|
28 |
姜楠.基于机器学习预测急性缺血性脑卒中静脉溶栓后出血转化的研究[D].长春:吉林大学,2020.
|
29 |
陈美西,徐昉,林时辉.影像组学在肺部炎症性疾病中的研究进展[J/CD].中华危重症医学杂志(电子版),2023,16(3):245-249.
|
30 |
Ma H, Parsons MW, Christensen S, et al. A multicentre, randomized, double-blinded, placebo-controlled phase III study to investigate EXtending the time for Thrombolysis in Emergency Neurological Deficits (EXTEND)[J]. Int J Stroke, 2012, 7 (1): 74-80.
|
31 |
Campbell BCV, Ma H, Ringleb PA, et al. Extending thrombolysis to 4.5-9 h and wake-up stroke using perfusion imaging: a systematic review and meta-analysis of individual patient data[J]. Lancet, 2019, 394 (10193): 139-147.
|
32 |
包婉秋,彭霞,张春霞,等.非增强CT纹理特征对超急性期脑梗死溶栓后出血转化的预测价值[J].中国中西医结合影像学杂志,2022,20(2):122-127.
|
33 |
Miller MI, Orfanoudaki A, Cronin M, et al. Natural language processing of radiology reports to detect complications of ischemic stroke[J]. Neurocrit Care, 2022, 37 (Suppl 2): 291-302.
|
34 |
Yu Y, Guo D, Lou M, et al. Prediction of hemorrhagic transformation severity in acute stroke from source perfusion MRI[J]. IEEE Trans Biomed Eng, 2018, 65 (9): 2058-2065.
|
35 |
van Voorst H, Konduri PR, van Poppel LM, et al. Unsupervised deep learning for stroke lesion segmentation on follow-up CT based on generative adversarial networks[J]. AJNR Am J Neuroradiol, 2022, 43 (8): 1107-1114.
|
36 |
郭晓慧.基于PROs和决策树方法构建慢性肾脏病证候诊断工具的研究[D].广州:广州中医药大学,2016.
|
37 |
Dharmasaroja P, Dharmasaroja PA. Prediction of intracerebral hemorrhage following thrombolytic therapy for acute ischemic stroke using multiple artificial neural networks[J]. Neurol Res, 2012, 34 (2): 120-128.
|
38 |
丁少华.基于多模态MRI构建急性缺血性脑卒中机械取栓术后预后预测模型及影像组学研究[D].苏州:苏州大学,2021.
|
39 |
Jiang L, Zhou L, Yong W, et al. A deep learning-based model for prediction of hemorrhagic transformation after stroke[J]. Brain Pathol, 2023, 33 (2): e13023.
|
40 |
徐伟,李辉萍,贺国华,等.利用人工智能系统预测脑梗死静脉溶栓后的出血转化[J].中国卫生统计,2021,38(2):250-253.
|
41 |
Slevin M, Matou S, Zeinolabediny Y, et al. Monomeric C-reactive protein—a key molecule driving development of Alzheimer's disease associated with brain ischaemia?[J]. Sci Rep, 2015 (5): 13281.
|
42 |
Shinoda N, Hori S, Mikami K, et al. Prediction of hemorrhagic transformation after acute thrombolysis following major artery occlusion using relative ADC ratio: a retrospective study[J]. J Neuroradiol, 2017, 44 (6): 361-366.
|
43 |
陈茹,王胜锋,周家琛,等.预测模型研究的偏倚风险和适用性评估工具解读[J].中华流行病学杂志,2020,41(5):776-781.
|