机器学习算法在压力性损伤管理中的应用进展

doi:10.3761/j.issn.0254-1769.2021.02.009

摘要/Abstract

摘要：

随着护理信息化管理的不断推进,数量庞大的多重结构数据的收集和重新利用与人工智能领域密切结合已成为趋势。压力性损伤在管理方面存在大量多重结构数据,其管理方法与人工智能领域的结合已从前沿技术逐渐转变到现实应用阶段,推动着压力性损伤管理由“制度管理”向“数据管理”转变。该文从应用基础、测量和分析创面、风险预测模型3个方面,对机器学习算法在压力性损伤中的应用研究进行综述,旨在为推动压力性损伤信息化管理提供参考。

关键词: 压力性损伤, 机器学习, 信息化管理, 综述, 护理

Abstract:

With the continuous advancement of nursing information management,the collection and reuse of a large number of multi-structure data and the close integration of the field of artificial intelligence have become a trend. There is a large number of multi-structure data in the management of pressure injury. The integration has gradually changed from cutting-edge technology to the actual application stage,and promoted the transformation of pressure injury management from "system management" to "data management". This article summarizes the application research of machine learning algorithms in pressure injury in the field of artificial intelligence from 3 aspects,namely application foundation,measuring and analyzing wounds,risk prediction model,and aims to promote the information management of pressure injury for references.

Key words: Pressure Injury, Machine Learning, Information Management, Review, Nursing Care

曲超然, 王青, 韩琳, 姜小鹰. 机器学习算法在压力性损伤管理中的应用进展[J]. 中华护理杂志, 2021, 56(2): 212-217.

QU Chaoran, WANG Qing, HAN Lin, JIANG Xiaoying. A literature review on the application of machine learning algorithms in pressure injury management[J]. Chinese Journal of Nursing, 2021, 56(2): 212-217.

图/表 1

参考文献 43

[1]	Gefen A, Weihs D . Cytoskeleton and plasma-membrane damage resulting from exposure to sustained deformations:a review of the mechanobiology of chronic wounds[J]. Med Eng Phys, 2016,8(9):828-833.
[2]	Hartmann CW, Solomon J, Palmer JA , et al. Contextual facilitators of and barriers to nursing home pressure ulcer prevention[J]. Adv Skin Wound Care, 2016,29(5):226-238.
[3]	江小琼, 蔡福满, 侯祥庆 , 等. 皮肤温度监测在压力性损伤风险预警中的应用研究[J]. 中华护理杂志, 2020,55(1):32-38.
	Jiang XQ, Cai FM, Hou XQ , et al. Application research of skin temperature monitoring in early warning of pressure injury[J]. Chin J Nurs, 2020,55(1):32-38.
[4]	Sun X, Ni P, Wu M , et al. A clinical epidemiological profile of chronic wounds in wound healing department in Shanghai[J]. Int J Low Extremity Wounds, 2017,16(1):36-44.
[5]	European Pressure Ulcer Advisory Panel, National Pressure Injury Advisory Panel, Pan Pacific Pressure Injury Alliance. Prevention and treatment of pressure ulcers/injuries:clinical practice guideline[S]. EPUAP/NPIAP/PPPIA: 2019.
[6]	Westbrook JI, Ling L, Lehnbom EC , et al. What are incident reports telling us? A comparative study at two Australian hospitals of medication errors identified at audit,detected by staff and reported to an incident system[J]. Int J Qual Health Care, 2015,27(1):1-9.
[7]	张文娴, 崔妙玲, 应燕萍 . 构建医院护理差错及不良事件报告系统的研究进展[J]. 中华护理杂志, 2008,43(12):1142-1144.
	Zhang WX, Cui ML, Ying YP . Establishment of nursing errors and adverse events reporting system in hospitals[J]. Chin J Nurs, 2008,43(12):1142-1144.
[8]	陈越, 皮红英, 宋杰 , 等. 临床决策支持系统在脑卒中患者护理中的应用进展[J]. 中华护理杂志, 2019,54(12):1898-1901.
	Chen Y, Pi HY, Song J , et al. Research progress on clinical decision support system for stroke nursing[J]. Chin J Nurs, 2019,54(12):1898-1901.
[9]	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.
	Ding SQ, Lu J, Qin CX , et al. Application progress on data mining for nursing adverse event management[J]. Chin J Nurs, 2019,54(6):873-877.
[10]	李萍, 史婷奇, 陆瑶 , 等. 护士长决策护理质量指标管理系统的构建[J]. 中华护理杂志, 2019,54(10):1540-1545.
	Li P, Shi TQ, Lu Y , et al. Quality index management system for head nurse's decision based on business intelligence[J]. Chin J Nurs, 2019,54(10):1540-1545.
[11]	夏冬云, 史婷奇, 陆巍 , 等. 压力性损伤临床决策支持系统的研发与应用[J]. 中华护理杂志, 2020,55(1):50-54.
	Xia DY, Shi TQ, Lu W , et al. Development and application of clinical decision support system for pressure injury[J]. Chin J Nurs, 2020,55(1):50-54.
[12]	Agarwal R, Gao GD, Roches CD , et al. Research commentary:the digital transformation of healthcare:current status and the road ahead[J]. Inform Syst Res, 2010,21(4):796-809.
[13]	Ward MJ, Marsolo KA, Froehle CM . Applications of business analytics in healthcare[J]. Bus Horiz, 2014,57(5):571-582.
[14]	Wang YC, Hajli N . Exploring the path to big data analytics success in healthcare[J]. J Bus Res, 2017,70:287-299.
[15]	Jiang P, Winkley J, Zhao C , et al. An intelligent information forwarder for healthcare big data systems with distributed wearable sensors[J]. Ieee Syst J, 2016,10(3):1147-1159.
[16]	Srinivasan U, Arunasalam B . Leveraging big data analytics to reduce healthcare costs[J]. It Prof, 2013,15(6):21-28.
[17]	Pramanik I, Lau RYK, Azad AK , et al. Healthcare informatics and analytics in big data[J]. Expert Syst Appl, 2020,152(15):113-388.
[18]	Goodwin TR, Dina DF . A customizable deep learning model for nosocomial risk prediction from critical care notes with indirect supervision[J]. J Am Med Inform Assn, 2020,27(4):567-576.
[19]	Zahia S, Garcia Zapirain MB, Sevillano X , et al. Pressure injury image analysis with machine learning techniques:a systematic review on previous and possible future methods[J]. Artif Intell Med, 2020,102:101742.
[20]	Raju D, Su X, Patrician PA , et al. Exploring factors associated with pressure ulcers:a data mining approach[J]. Int J Nurs Stud, 2014,52(1):102-111.
[21]	Munoz N, Posthauer ME, Cereda E , et al. The role of nutrition for pressure injury prevention and healing:the 2019 international clinical practice guideline recommendations[J]. Adv Skin Wound Care, 2020,33(3):123-136.
[22]	Ortiz OP, Sierra-Sosa D, Zapirain BG . Pressure ulcer image segmentation technique through synthetic frequencies generation and contrast variation using toroidal geometry[J]. Bio Med Central, 2017,16(1):4.
[23]	Delode J, Rosow E, Roth C , et al. A wound-healing monitoring system[J]. IRBM, 2001,22(1):49-52.
[24]	Bochko V, Valisuo P, Harju T , et al. Lower extremity ulcer image segmentation of visual and near infrared imagery[J]. Skin Res Technol, 2010,16(2):190-197.
[25]	Veredas FJ, Mesa H, Morente L . Efficient detection of wound-bed and peripheral skin with statistical colour models[J]. Med Biol Eng Comput, 2015,53(4):345-359.
[26]	Wang CH, Yan XC, Smith M, et al. A unified framework for automatic wound segmentation and analysis with deep convolutional neural networks [C]. Milan:Conf Proc Ieee Eng Med Biol Soc, 2015: 2415-2418.
[27]	Zahia S, Sierra-Sosa D, Garcia-Zapirain B , et al. Tissue classification and segmentation of pressure injuries using convolutional neural networks[J]. Comput Meth Prog Bio, 2018,159:51-58.
[28]	Garcia-Zapirain B, Sierra-Sosa D, Ortiz D , et al. Efficient use of mobile devices for quantification of pressure injury images[J]. Technol Health Care, 2018,26(S1):269-280.
[29]	Kosmopoulos DI, Tzevelekou FL . Automated pressure ulcer lesion diagnosis for telemedicine systems[J]. Ieee Eng Med Biol, 2007,26(5):18-22.
[30]	Wannous H, Lucas Y, Treuillet S . Enhanced assessment of the wound-healing process by accurate multiview tissue classification[J]. Ieee T Med Imaging, 2011,30(2):315-326.
[31]	Veredas FJ, Luque-Baena RM, Martin-Santos FJ , et al. Wound image evaluation with machine learning[J]. Neurocomputing, 2015,164:112-122.
[32]	Kawahara J, Hamarneh G. Multi-resolution-tract CNN with hybrid pretrained and skin-lesion trained layers [C]. Springer:Intervention Workshop on Machine Learning in Medical Imaging, 2016: 164-171.
[33]	Litjens G, Kooi T, Bejnordi BE , et al. A survey on deep learning in medical image analysis[J]. Med Image Anal, 2017,42(9):60-88.
[34]	Shin HC, Roth HR, Gao M , et al. Deep convolutional neural networks for computer-aided detection:CNN,architectures,dataset characteristics and transfer learning[J]. Ieee T Med Imaging, 2016,35(5):1285-1298.
[35]	Demyanov S, Chakravorty R, Abedini M, et al. Classification of dermoscopy patterns using deep convolutional neural networks [C]//2016 IEEE 13th International Symposium on Biomedical Imaging(ISBI 2016). IEEE, 2016.
[36]	Goldstein BA, Navar AM, Pencina MJ , et al. Opportunities and challenges in developing risk prediction models with electronic health records data:a systematic review[J]. J Am Med Inform Assoc, 2017,24(1):198-208.
[37]	Wilson PW, D'Agostino RB, Levy D,et al. Prediction of coronary heart disease using risk factor categories[J]. Circulation, 1998,97(18):1837-1847.
[38]	Hersh WR, Weiner MG, Embi PJ , et al. Caveats for the use of operational electronic health record data in comparative effectiveness research[J]. Med Care, 2013,51(8):30-37.
[39]	Raju D, Su X, Patrician PA , et al. Exploring factors associated with pressure ulcers:a data mining approach[J]. Int J Nurs Stud, 2015,52(1):102-111.
[40]	Alderden J, Pepper GA, Wilson A , et al. Predicting pressure injury in critical care patients:a machine-learning model[J]. Am J Crit Care, 2018,27(6):461-468.
[41]	Setoguchi Y, Ghaibeh AA, Mitani K , et al. Predictability of pressure ulcers based on operation duration,transfer activity,and body mass index through the use of an alternating decision tree[J]. J Med Invest, 2016,63(3):248-255.
[42]	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.
	Song J . Predictive model construction and software development of skin damage care adverse events with big data technology[D]. Beijing:Medical School of Chinese PLA, 2018.
[43]	Goodwin TR, Demner-Fushman D . A customizable deep learning model for nosocomial risk prediction from critical care notes with indirect supervision[J]. J Am Med Inform Assoc, 2020,27(4):567-576.