1. |
孙健平, 张堃, 王鹏飞, 等. 骨折患者近端深静脉血栓形成临床特征及危险因素. 中华创伤杂志, 2019, 35(7): 625-630.
|
2. |
刘海龙, 王志聪, 陈曦, 等. 老年髋部骨折患者受伤至入院时间与下肢深静脉血栓形成的关系. 山东医药, 2022, 62(17): 69-72.
|
3. |
夏芊, 向小容, 谢仟, 等. 机器学习在静脉血栓栓塞疾病中的研究进展. 四川医学, 2023, 44(1): 78-81.
|
4. |
Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet, 2020, 395(10236): 1579-1586.
|
5. |
Matuchansky C. Deep medicine, artificial intelligence, and the practising clinician. Lancet, 2019, 394(10200): 736.
|
6. |
Liu H, Yuan H, Wang Y, et al. Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients. Sci Rep, 2021, 11(1): 12868.
|
7. |
孙林凯, 金家善, 耿俊豹. 基于修正邓氏灰色关联度的设备费用影响因素分析. 数学的实践与认识, 2012, 42(8): 140-145.
|
8. |
袁晨杰, 田侃. 基于灰色关联度分析法的四川省人均卫生费用影响因素研究. 中国医疗管理科学, 2022, 12(1): 23-28.
|
9. |
果爽, 程伟, 刘鑫鑫, 等. 基于 BP 神经网络的医疗卫生服务质量满意度研究. 中国医学物理学杂志, 2023, 40(6): 788-792.
|
10. |
Diao S, Li J, Zhao J, et al. Risk factors and new inflammatory indicators of deep vein thrombosis after adult patella fractures. Front Surg, 2022, 9: 1028542.
|
11. |
Dou C, Li T, Yang S, et al. Epidemiological status and risk factors of deep vein thrombosis in patients with femoral neck fracture. J Orthop Surg Res, 2022, 17(1): 41.
|
12. |
Niu S, Pei Y, Hu X, et al. Relationship between the neutrophil-to-lymphocyte ratio or platelet-to-lymphocyte ratio and deep venous thrombosis (DVT) following femoral neck fractures in the elderly. Front Surg, 2022, 9: 1001432.
|
13. |
Melinte RM, Arbănași EM, Blesneac A, et al. Inflammatory biomarkers as prognostic factors of acute deep vein thrombosis following the total knee arthroplasty. Medicina (Kaunas), 2022, 58(10): 1502.
|
14. |
张巧云, 李音音, 蒙杰, 等. 中性粒细胞相关参数在下肢深静脉血栓形成的临床意义. 中国实验诊断学, 2021, 25(6): 833-836.
|
15. |
Wang G, Zhao W, Zhao Z, et al. Leukocyte as an independent predictor of lower-extremity deep venous thrombosis in elderly patients with primary intracerebral hemorrhage. Front Neurol, 2022, 13: 899849.
|
16. |
Wang P, Wang Y, Yuan Z, et al. Venous thromboembolism risk assessment of surgical patients in Southwest China using real-world data: establishment and evaluation of an improved venous thromboembolism risk model. BMC Med Inform Decis Mak, 2022, 22(1): 59.
|
17. |
徐松, 叶哲伟. 人工智能在骨科的应用现状及前景. 中国医刊, 2019, 54(2): 117-119.
|
18. |
Xiang YF, Zhao LQ, Liu ZZ, et al. Implementation of artificial intelligence in medicine: status analysis and development suggestions. Artif Intell Med, 2020, 102: 101780.
|
19. |
Martins TD, Annichino-Bizzacchi JM, Romano AVC, et al. Artificial neural networks for prediction of recurrent venous thromboembolism. Int J Med Inform, 2020, 141: 104221.
|
20. |
高远, 潘晓英, 李建涛, 等. 人工智能算法模型在创伤患者下肢静脉血栓栓塞症诊断中的预测价值. 中华创伤杂志, 2021, 37(10): 932-937.
|
21. |
周武, 曹发奇, 曾睿寅, 等. 创伤骨科患者围术期下肢静脉血栓形成诊断及防治专家共识(2022 年). 中华创伤杂志, 2022, 38(1): 23-31.
|
22. |
王艺燕, 何凌霄, 欧阳朝威, 等. 创伤患者静脉血栓栓塞症风险评估工具的研究进展. 军事护理, 2023, 40(2): 95-97.
|