护理学报 ›› 2021, Vol. 28 ›› Issue (7): 30-34.doi: 10.16460/j.issn1008-9969.2021.07.030
刘雨安1, 杨小文1, 李乐之2
摘要: 目的 了解机器学习的基本概念以及在疾病预测中的应用,以期为临床的信息化建设与发展提供参考。方法 检索并查阅相关文献,分析总结机器学习的概念以及相关应用的研究。结果 疾病预测模型的构建是机器学习常见的应用之一,目前研究多集中于疾病发生风险的预测,预后风险分层的预测,慢性病进展的预测以及治疗效果的预测。结论 机器学习由于其强大的数据分析与探索能力,在疾病预测及辅助临床决策方面具有显著的优越性。目前,我国护理学科信息化、智能化的发展尚处于起步阶段,将机器学习技术用于指导临床护理工作的研究较为缺乏。未来应借鉴国外相关研究成果,构建适合我国使用的机器学习预测模型,探索人工智能与护理工作的结合与辅助,并加强信息技术相关人才的培养,是下一步的研究方向。
中图分类号:
[1] 中华人民共和国国务院. 国务院关于印发新一代人工智能发展规划的通知[EB/OL].[2017-07-20](2020-08-25).http://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm. [2] Deo RC.Machine Learning in Medicine[J]. Circulation, 2015,132(20):1920-1930. DOI:10.1161/circulationaha.115.001593. [3] DeGregory KW, Kuiper P, DeSilvio T, et al. A Review of Machine Learning in Obesity[J]. Obes Rev, 2018, 19(5):668-685. DOI:10.1111/obr.12667. [4] Alanazi HO, Abdullah AH, Qureshi KN.A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care[J]. J Med Syst, 2017,41(4):69. DOI:10.1007/s10916-017-0715-6. [5] Triantafyllidis A, Polychronidou E,Alexiadis A,et al.Computerized Decision Support and Machine Learning Applications for the Prevention and Treatment of Childhood Obesity: A Systematic Review of the Literature[J]. Artif Intell Med, 2020,104:101844.DOI:10.1016/j.artmed.2020.101844. [6] Islam MM, Nasrin T, Walther BA,et al.Prediction of Sepsis Patients Using Machine Learning Approach: A Meta-analysis[J]. Comput Methods Programs Biomed, 2019,170:1-9. DOI:10.1016/j.cmpb.2018.12.027. [7] Awan SE, Sohel F, Sanfilippo FM, et al.Machine Learning in Heart Failure: Ready for Prime Time[J]. Curr Opin Cardiol,2018,33(2):190-195.DOI:10.1097/HCO.0000000000000491. [8] Lee A, Taylor P, Kalpathy-Cramer J, et al.Machine Learning Has Arrived![J]. Ophthalmology, 2017,124(12):1726-1728.DOI:10.1016/j.ophtha.2017.08.046. [9] Lee S, Mohr NM, Street WN, et al.Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview[J]. West J Emerg Med, 2019,20(2):219-227. DOI:10.5811/westjem.2019.1.41244. [10] 丁四清,陆晶,秦春香,等. 数据挖掘在护理不良事件管理中的应用进展[J]. 中华护理杂志, 2019,54(6):873-877.DOI:10.3761/j.issn.0254-1769.2019.06.013. [11] Levin S, Toerper M, Hamrock E, et al.Machine-learning-Based Electronic Triage More Accurately Differentiates Patients with Respect to Clinical Outcomes Compared with the Emergency Severity Index[J]. Ann Emerg Med, 2018,71(5):565-574.DOI:10.1016/j.annemergmed.2017.08.005. [12] 苗晓,马靓,徐萍,等. ICU获得性衰弱风险预测模型的研究进展[J]. 中国护理管理, 2019,19(1):146-150. DOI:10.3969/j.issn.1672-1756.2019.01.033. [13] Barton C, Chettipally U, Zhou Y, et al.Evaluation of a Machine Learning Algorithm for Up to 48-hour Advance Prediction of Sepsis Using Six Vital Signs[J].Comput Biol Med, 2019,109:79-84. DOI:10.1016/j.compbiomed.2019.04.027. [14] Mohamadlou H, Lynn-Palevsky A, Barton C, et al.Prediction of Acute Kidney Injury with a Machine Learning Algorithm Using Electronic Health Record Data[J]. Can J Kidney Health Dis,2018,5:2054358118776326.DOI:10.1177/2054358118776326. [15] Wang Y, Lei L, Ji M, et al.Predicting Postoperative Delirium after Microvascular Decompression Surgery with Machine Learning[J]. J Clin Anesth, 2020,66:109896.DOI:10.1016/j.jclinane.2020.109896. [16] Corradi JP, Thompson S, Mather JF, et al.Prediction of Incident Delirium Using a Random Forest Classifier[J]. J Med Syst,2018,42(12):261.DOI:10.1007/s10916-018-1109-0. [17] Taylor RA, Moore CL, Cheung KH, et al.Predicting Urinary Tract Infections in the Emergency Department with Machine Learning[J]. PLoS One, 2018,13(3):e0194085. DOI:10.1371/journal.pone.0194085. [18] Ong ME, Lee Ng CH, Goh K, et al.Prediction of Cardiac Arrest In Critically Ill Patients Presenting to the Emergency Department Using a Machine Learning Score Incorporating Heart Rate Variability Compared with the Modified Early Warning Score[J]. Crit Care, 2012, 16(3):R108.DOI:10.1186/cc11396. [19] Frizzell JD, Liang L, Schulte PJ, et al.Prediction Of 30-Day All-cause Readmissions in Patients Hospitalized For Heart Failure: Comparison of Machine Learning and Other Statistical Approaches[J]. JAMA Cardiol, 2017,2(2):204-209. DOI:10.1001/jamacardio.2016.3956. [20] Rojas JC, Carey KA, Edelson DP,et al, Predicting Intensive Care Unit Readmission with Machine Learning Using Electronic Health Record Data[J]. Ann Am Thorac Soc, 2018, 15(7):846-853. DOI:10.1513/AnnalsATS.201710-787OC. [21] 王振容,蒋晓莲. 数据挖掘在护理领域中的应用进展[J]. 中华护理杂志,2017,52(10):1262-1264.DOI:10.3761/j.issn.0254-1769.2017.10.022. [22] 周丹,尹安春. 人工神经网络在护理领域的应用研究进展[J]. 护理学杂志,2020,35(3):94-97. DOI:10.3870/j.issn.1001-4152.2020.03.094. [23] Linthicum KP, Schafer KM, Ribeiro JD.Machine Learning in Suicide Science: Applications and Ethics[J]. Behav Sci Law, 2019,37(3):214-222. DOI:10.1002/bsl.2392. [24] Su D, Zhang X, He K, et al.Use Of Machine Learning Approach to Predict Depression in the Elderly in China: A Longitudinal Study[J]. J Affect Disord, 2020,282:289-298. DOI:10.1016/j.jad.2020.12.160. [25] Fernandes M,Mendes R,Vieira SM,et al.Predicting Intensive Care Unit Admission Among Patients Presenting to the Emergency Department Using Machine Learning and Natural Language Processing[J]. PLoS One, 2020,15(3):e0229331. DOI:10.1371/journal.pone.0229331. [26] Parker CA,Liu N,Wu SX, et al.Predicting Hospital Admission at the Emergency Department Triage: A Novel Prediction Model[J]. Am J Emerg Med, 2019,37(8):1498-1504. DOI:10.1016/j.ajem.2018.10.060. [27] Gao Y, Cai GY, Fang W, et al.Machine Learning Based Early Warning System Enables Accurate Mortality Risk Prediction for Covid-19[J]. Nat Commun, 2020, 11(1):5033. DOI:10.1038/s41467-020-18684-2. [28] Cheng FY, Joshi H,Tandon P,et al.Using Machine Learning to Predict ICU Transfer in Hospitalized Covid-19 Patients[J]. J Clin Med, 2020,9(6):1668.DOI:10.3390/jcm9061668. [29] Vaid A, Somani S, Russak AJ, et al.Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients with Covid-19 in New York City: Model Development and Validation[J]. J Med Internet Res, 2020,22(11):e24018. DOI:10.2196/24018. [30] World Health Organization. Hypertension[EB/OL].(2021-01-17). https://www.who.int/health-topics/hypertension/. [31] Chang W, Liu Y, Xiao Y, et al.A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data[J]. Diagnostics (Basel), 2019,9(4):178. DOI:10.3390/diagnostics9040178. [32] Weng SF, Reps J, Kai J, et al.Can Machine-Learning Improve Cardiovascular Risk Prediction Using Routine Clinical Data?[J]. PLoS One, 2017,12(4):e0174944. DOI:10.1371/journal.pone.0174944. [33] Lee W, Lee J, Lee H, et al.Prediction of Hypertension Complications Risk Using Classification Techniques[J]. Ind Eng Manage,2014,13(4):449-453. DOI:10.7232/iems.2014.13.4.449. [34] World Health Organization.Diabetes[EB/OL].(2021-01-17). https://www.who.int/health-topics/diabetes#tab=tab_1. [35] Georga EI, Protopappas VC, Ardigo D, et al.A Glucose Model Based on Support Vector Regression for the Prediction of Hypoglycemic Events Under Free-living Conditions[J]. Diabetes Technol Ther, 2013, 15(8):634-643.DOI:10.1089/dia.2012.0285. [36] Zeevi D, Korem T, Zmora N, et al.Personalized Nutrition by Prediction of Glycemic Responses[J]. Cell, 2015,163(5):1079-1094. DOI:10.1016/j.cell.2015.11.001. [37] Zhu T, Li K, Herrero P, et al. Deep Learning for Diabetes: A Systematic Review[J]. IEEE J Biomed Health Inform, 2020,PP. DOI:10.1109/jbhi.2020.3040225. [38] Dagliati A, Marini S, Sacchi L, et al.Machine Learning Methods to Predict Diabetes Complications[J]. J Diabetes Sci Technol,2018,12(2):295-302.DOI:10.1177/1932296817706375. [39] Orchard P,Agakova A,Pinnock H,et al.Improving Prediction of Risk of Hospital Admission in Chronic Obstructive Pulmonary Disease: Application of Machine Learning to Telemonitoring Data[J]. J Med Internet Res, 2018,20(9):e263. DOI:10.2196/jmir.9227. [40] Wang T, Qiu RG, Yu M.Predictive Modeling of the Progression of Alzheimer’s Disease with Recurrent Neural Networks[J]. Sci Rep, 2018, 8(1):9161. DOI:10.1038/s41598-018-27337-w. [41] Shi HY, Tsai JT, Chen YM, et al.Predicting Two-year Quality of Life after Breast Cancer Surgery Using Artificial Neural Network and Linear Regression Models[J]. Breast Cancer Res Treat, 2012, 135(1):221-229. DOI:10.1007/s10549-012-2174-6. [42] Bica I,Alaa AM,Lambert C, et al.From Real-world Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges[J]. Clin Pharmacol Ther, 2021,109(1):87-100. DOI:10.1002/cpt.1907. [43] 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. [44] Tahmassebi AWG,Helbich TH.Impact of Machine Learning with Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients[J]. Invest Radiol, 2019,54(2):110-117. DOI:10.1097/RLI.0000000000000518. [45] 赵飞,兰蓝,曹战强,等. 我国人工智能在健康医疗领域应用发展现状研究[J]. 中国卫生信息管理杂志,2018,15(3):344-349.DOI:10.3969/j.issn.1672-5166.2018.03.024. |
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