基于电子病历数据的风险预测模型在临床护理中的应用现状

doi:10.16460/j.issn1008-9969.2020.16.021

摘要/Abstract

摘要： 目的总结归纳基于电子病历数据的风险预测模型在临床护理应用进展,为风险预测模型的构建提供参考。方法搜索国内外关于风险预测模型的应用研究文献,从模型构建方式、模型性能指标、应用的护理领域等角度进行综述。结果基于电子病历数据的风险预测模型在护理领域有一定的开展,能较好地识别患者风险,对护理临床工作具有指导作用,但存在模型构建方式较单一,新技术应用不足,非结构化数据的全面有效提取仍有一定难度、模型性能评价指标不够完善的问题。结论风险预测模型能在一定程度上识别患者风险,利于护理工作的提前介入和干预,能够更加科学、有效的指导护理临床工作,改善患者结局。

关键词: 电子病历, 风险预测, 预测模型, 数据挖掘

中图分类号:

旷小羿, 侯惠如. 基于电子病历数据的风险预测模型在临床护理中的应用现状[J]. 护理学报, 2020, 27(16): 21-24.

参考文献

[1] 杨淼淇,柴华,喻革武. 数字化医院的发展趋势和建设要素[J]. 医学信息, 2010, 23(3):555-556.
[2] Bates DW, Saria S, Ohno-Machado L, et al.Big Data in Health Care: Using Analytics to Identify and Manage High-Risk and High-Cost Patients[J]. Health Aff (Millwood), 2014, 33(7):1123-1131. DOI:10.1377/hlthaff.2014.0041.
[3] Shickel B, Tighe PJ, Bihorac A, et al.Deep Ehr: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record(Ehr)Analysis[J].IEEE J Biomed Health Inform,2018,22(5):1589-1604.DOI:10.1109/JBHI.2017.2767063.
[4] Price WN, Cohen IG.Privacy in the Age of Medical Big Data[J]. Nat Med, 2019, 25(1):37-43. DOI:10.1038/s41591-018-0272-7.
[5] Nelson-Brantley HV, Jenkins P, Chipps E.Turning Health Systems Data into Actionable Information[J]. J Nurs Adm, 2019,49(4):176-178.DOI:10.1097/NNA.0000000000000734.
[6] Austin PC, Tu JV, Ho JE, et al.Using Methods from the Data-Mining and Machine-Learning Literature for Disease Classification and Prediction: A Case Study Examining Classification of Heart Failure Subtypes[J]. J Clin Epidemiol, 2013, 66(4):398-407. DOI:10.1016/j.jclinepi.2012.11.008.
[7] Shah P, Kendall F, Khozin S, et al.Artificial Intelligence and Machine Learning in Clinical Development: A Translational Perspective[J]. NPJ Digit Med, 2019, 2:69. DOI:10.1038/s41746-019-0148-3.
[8] Kohli M, Prevedello LM, Filice RW, et al.Implementing Machine Learning in Radiology Practice and Research[J]. AJR Am J Roentgenol, 2017, 208(4):754-760. DOI:10.2214/ajr.16.17224.
[9] Ge Y, Wang Q, Wang L, et al.Predicting Post-Stroke Pneumonia Using Deep Neural Network Approaches[J].Int J Med Inform, 2019,132(1):33.DOI:10.1016/j.ijmedinf.2019.103986.
[10] Rochefort CM, Buckeridge DL, Forster AJ.Accuracy of Using Automated Methods for Detecting Adverse Events from Electronic Health Record Data:A Research Protocol[J]. Implement Sci, 2015, 10(5):9. DOI:10.1186/s13012-014-0197-6.
[11] 陈沅,吴蓓雯,钱蒨健,等. 成人心血管手术压疮高危预测模型的建立与验证[J]. 护理学杂志, 2019, 34(10):52-54.
[12] 黄华平,何海燕,陈斌,等. 失禁性皮炎风险预测模型的构建及验证研究[J]. 中西医结合护理(中英文), 2019, 5(5):1-5. DOI:10.11997/nitcwm.201905001.
[13] 杨青,王国蓉,江宾,等. 基于决策树的肿瘤患者难免性压疮风险预测模型研究[J]. 护理学杂志, 2019, 34(13): 4-7. DOI:10.3870/j.issn.1001-4152.2019.13.004.
[14] Moon M, Lee SK.Applying of Decision Tree Analysis to Risk Factors Associated with Pressure Ulcers in Long-Term Care Facilities[J]. Healthc Inform Res, 2017, 23(1): 43-52. DOI:10.4258/hir.2017.23.1.43.
[15] 宋杰. 基于大数据技术的皮肤损伤护理不良事件预测模型的构建和平台研发[D]. 北京:中国人民解放军医学院, 2018.
[16] Park JI, Bliss DZ, Chi CL, et al.Knowledge Discovery with Machine Learning for Hospital-Acquired Catheter-Associated Urinary Tract Infections[J].Comput Inform Nurs, 2019, 38(1):8. DOI:10.1097/CIN.0000000000000562.
[17] 刘芬. 老年住院患者PICC相关深静脉血栓危险因素分析及风险评估模型构建[D]. 北京:中国人民解放军医学院, 2017.
[18] 韩莹,李娜. Nomogram预测模型分析早产儿PICC置管并发静脉炎症的风险[J]. 川北医学院学报, 2019, 34(2): 309-312. DOI:10. 3969/j. issn. 1005-3697. 2019. 02.41.
[19] 邢霞,皮红英,郭晓菊. 颅内破裂动脉瘤术前再出血风险预测模型研究[J]. 神经损伤与功能重建, 2017, 12(5): 407-409. DOI:10.16780/j.cnki.sjssgncj.2017.05.009.
[20] 安莹,王艳玲. 慢性阻塞性肺疾病急性加重期患者短期预后预测模型的建立[J]. 中华护理杂志, 2019, 54(1):42-46. DOI:10.3761/j.issn.0254-1769.2019.01.007.
[21] 王娜,李娟,李霞,等. 肝硬化患者肝性脑病风险预测模型的构建及应用研究[J]. 中华护理杂志, 2019, 54(6):805-811. DOI:10.3761/j.issn.0254-1769.2019.06.001.
[22] 张灵芳. 待产期孕妇的风险评估及预测模型的构建[D]. 郑州:郑州大学, 2019.
[23] 张政,杨金红,荆晨晨,等. 肺癌晚期患者死亡风险预测评分系统的构建及评价[J]. 护士进修杂志, 2019, 34(23): 2122-2126. DOI:10.16821 /j. cnki. hsjx.2019.23.003.
[24] 普鹰,张莹,汤佳骏,等. 腹腔镜手术患者术中低体温预测模型的构建及应用[J]. 中华护理杂志, 2019,54(9):1308-1312. DOI:10.3761/j.issn.0254-1769.2019.09.005.
[25] 张家妍,李丽. ICU颅脑损伤术后患者便秘风险预测模型的构建[J]. 临床与病理杂志, 2018, 38(2):329-334. DOI: 10.3978/j.issn.2095-6959.2018.02.017.
[26] Ge Y, Wang Q, Wang L, et al.Predicting Post-Stroke Pneumonia Using Deep Neural Network Approaches[J]. Int J Med Inform, 2019, 132:103986. DOI:10.1016/j.ijmedinf.2019.103986.
[27] CUI J.Overview of Risk Prediction Models in Cardiovascular Disease Research[J]. Ann Epidemiol, 2009, 19(10): 711-717. DOI:10.1016/j.annepidem.2009.05.005.
[28] Wessler BS, Lai Yh L, Kramer W, et al.Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database[J]. Circ Cardiovasc Qual Outcomes, 2015, 8(4):368-375. DOI:10.1161/circoutcomes.115.001693.
[29] 栗伟,赵大哲,李博,等. Crf与规则相结合的医学病历实体识别[J]. 计算机应用研究, 2015, 32(4):1082-1086. DOI: 10.3969 /j. issn.1001-3695. 2015. 04.029.
[30] 包小源,黄婉晶,张凯,等. 非结构化电子病历中信息抽取的定制化方法[J]. 北京大学学报(医学版), 2018, 50(2):256-263.DOI:10.3969 /j.issn.1671-167X.2018.02.010.
[31] Hong N, Wen A, Shen F, et al.Integrating Structured and Unstructured Ehr Data Using an Fhir-based Type System: A Case Study with Medication Data[J]. AMIA Jt Summits Transl Sci Proc, 2018, 2017(1):74-83.
[32] Malmasi S,Hosomura N,Chang LS, et al.Extracting Healthcare Quality Information from Unstructured Data[J]. AMIA Annu Symp Proc, 2017, 2018(1):1243-1252.
[33] 宋杰,章洁,高远,等. 护理不良事件非结构上报内容的自然语言处理及效果比较[J]. 护理学报, 2018, 25(3):1-4.
[34] Zhang C, Ma R, Sun S, et al. Optimizing the Electronic Health Records through Big Data Analytics: A Knowledge-Based View[J]. IEEE Access, 2019, 7:136223-136231. DOI:10.1109/access.2019.2939158.
[35] Tubaishat A.Perceived Usefulness and Perceived Ease of Use of Electronic Health Records among Nurses: Application of Technology Acceptance Model[J]. Inform Health Soc Care,2018, 43(4):379-389.DOI:10.1080/17538157.2017.1363761.
[36] Brom H, Brooks Carthon JM, Ikeaba U, et al.Leveraging Electronic Health Records and Machine Learning to Tailor Nursing Care for Patients at High Risk for Readmissions[J]. J Nurs Care Qual, 2020, 35(1):27-33. DOI:10.1097/NCQ.0000000000000412.

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