健康推荐系统在癌症患者照护中应用的范围综述

doi:10.3761/j.issn.0254-1769.2023.14.016

摘要/Abstract

摘要：

目的对健康推荐系统（health recommender system，HRS）在癌症患者照护领域中的研究进行范围综述。方法遵循范围综述的方法学，系统检索PubMed、Web of Science、Cochrane Library、CINAHL、Embase、中国生物医学文献数据库、中国知网、万方数据库及维普中文期刊数据库中的相关文献。检索时限为建库至2022年5月，并对纳入文献进行总结和分析。结果共纳入12篇文献，HRS在癌症患者照护领域的应用涉及提供疾病、治疗及健康保健相关信息，自我护理指导、症状管理、决策支持、远程居家管理及随访。评价指标主要涉及可用性、有效性、准确性及安全性。结论 HRS可为癌症患者提供智能化、个性化照护支持，但在癌症领域的研究还处于起步阶段，未来有必要拓展HRS在癌症患者照护领域中的应用，融入相关理论构建HRS，进一步完善评价指标，促进其在临床中的应用。

关键词: 健康推荐系统, 癌症, 照护, 范围综述, 肿瘤护理

Abstract:

Objective To review the research status of Health Recommender System（HRS） in the field of cancer patient care. Methods Following the methodology of the scoping review，relevant literature was searched in Pubmed，Medline，Web of Science，Cochrane Library，CINAHL，Embase，China Biomedical Literature Database，CNKI，Wanfang and VIP Chinese journal databases. The search period was from the establishment until May 2022，and the included articles were summarized and analyzed. Results A total of 12 articles were included，and the application of HRS in the field of cancer patient care involved recommended diseases，treatment and health care related information，patient self-care guidance，symptom management，decision support，remote home management and follow-up. The evaluation indicators mainly include usability，effectiveness，accuracy and safety. Conclusion HRS can provide intelligent and personalized care support for cancer patients，but the research in the field of cancer is still in its infancy，and it is necessary to expand the application of HRS in the field of cancer patient care in the future，to integrate relevant theories to build HRS，to further improve the evaluation index，and to promote clinical application.

Key words: Health Recommender System, Cancer, Nursing, Scoping Review, Oncologic Nursing

赵喜娟, 杨明莹, 边燕, 张江, 吴光柳, 钱阳凤. 健康推荐系统在癌症患者照护中应用的范围综述[J]. 中华护理杂志, 2023, 58(14): 1780-1786.

ZHAO Xijuan, YANG Mingying, BIAN Yan, ZHANG Jiang, WU Guangliu, QIAN Yangfeng. Application of Health Recommender System in nursing care of cancer patients：a scoping review[J]. Chinese Journal of Nursing, 2023, 58(14): 1780-1786.

图/表 1

参考文献 29

[1]	Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020:GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3):209-249. DOI URL
[2]	刘宗超, 李哲轩, 张阳, 等. 2020全球癌症统计报告解读[J]. 肿瘤综合治疗电子杂志, 2021, 7(2):1-14.
	Liu ZC, Li ZX, Zhang Y, et al. Interpretation on the report of global cancer statistics 2020[J]. J Multidiscip Cancer Manag Electron Version, 2021, 7(2):1-14.
[3]	石习习, 王静, 杨雪琴. 癌症患者心理痛苦预测模型的构建及验证[J]. 中华护理杂志, 2022, 57(11):1330-1336. DOI URL
	Shi XX, Wang J, Yang XQ. Construction and validation of risk prediction model of psychological distress in patients with cancer[J]. Chin J Nurs, 2022, 57(11):1330-1336. DOI URL
[4]	胡琪, 朱定局, 吴惠粦, 等. 智能推荐系统研究综述[J]. 计算机系统应用, 2022, 31(4):47-58.
	Hu Q, Zhu DJ, Wu HL, et al. Survey on intelligent recommendation system[J]. Comput Syst Appl, 2022, 31(4):47-58.
[5]	Cheung KL, Durusu D, Sui XC, et al. How recommender systems could support and enhance computer-tailored digital health programs:a scoping review[J]. Digit Health, 2019, 5:2055207618824727.
[6]	程远航, 赵纹静, 杜英魁, 等. 基于微信平台的孕幼健康问题设计与实现[J]. 物联网技术, 2022, 12(3):64-67.
	Cheng YH, Zhao WJ, Du YK, et al. Design and implementation of pregnant and infant health problems based on WeChat platform[J]. Internet Things Technol, 2022, 12(3):64-67.
[7]	Yera Toledo R, Alzahrani AA, Martínez L. A food recommender system considering nutritional information and user preferences[J]. IEEE Access, 2019, 7:96695-96711. DOI URL
[8]	Han Y, Han ZG, Wu JH, et al. Artificial intelligence recommendation system of cancer rehabilitation scheme based on IoT technology[J]. IEEE Access, 2020, 8:44924-44935. DOI URL
[9]	王婧婷, 袁长蓉. 基于循证的概括综述及其方法学简介[J]. 护理学杂志, 2017, 32(8):103-105.
	Wang JT, Yuan CR. Introduction of evidence-based scoping review and its methodology[J]. J Nurs Sci, 2017, 32(8):103-105.
[10]	Arksey H, O’Malley L. Scoping studies:towards a methodological framework[J]. Int J Soc Res Methodol, 2005, 8(1):19-32. DOI URL
[11]	Rathi M, Pareek V. Mobile based healthcare tool an integrated disease prediction & recommendation system[J]. Int J Knowl Syst Sci, 2019, 10(1):38-62. DOI URL
[12]	Jacobs M, Hopkins J, Mumber M, et al. Usability evaluation of an adaptive information recommendation system for breast cancer patients[J]. AMIA Annu Symp Proc, 2019, 2019:494-503. PMID
[13]	Iatraki G, Kondylakis H, Koumakis L, et al. Personal health information recommender:implementing a tool for the empowerment of cancer patients[J]. Ecancermedicalscience, 2018, 12:851.
[14]	Ormel I, Onu CC, Magalhaes M, et al. Using a mobile app-based video recommender system of patient narratives to prepare women for breast cancer surgery:development and usability study informed by qualitative data[J]. JMIR Form Res, 2021, 5(6):e22970. DOI URL
[15]	邢伟. 基于文本分类的肿瘤知识推荐系统的设计与实现[D]. 成都: 电子科技大学, 2020.
	Xing W. Design and implementation of tumor knowledge rec-ommendation system based on text classification[D]. Chengdu: University of Electronic Science and Technology of China, 2020.
[16]	Hassoon A, Baig Y, Naiman DQ, et al. Randomized trial of two artificial intelligence coaching interventions to increase physical activity in cancer survivors[J]. NPJ Digit Med, 2021, 4(1):168. DOI PMID
[17]	Monteiro-Guerra F, Signorelli GR, Tadas S, et al. A personalized physical activity coaching app for breast cancer survivors:design process and early prototype testing[J]. JMIR Mhealth Uhealth, 2020, 8(7):e17552. DOI URL
[18]	Kanera IM, Willems RA, Bolman CA, et al. Long-term effects of a web-based cancer aftercare intervention on moderate physical activity and vegetable consumption among early can-cer survivors:a randomized controlled trial[J]. Int J Behav Nutr Phys Act, 2017, 14(1):19. DOI PMID
[19]	Mooney K, Whisenant MS, Beck SL. Symptom care at home:a comprehensive and pragmatic PRO system approach to im-prove cancer symptom care[J]. Med Care, 2019, 57(Suppl 51):S66-S72. DOI URL
[20]	Beck SL, Eaton LH, Echeverria C, et al. SymptomCare@Home:developing an integrated symptom monitoring and management system for outpatients receiving chemotherapy[J]. Comput Inform Nurs, 2017, 35(10):520-529.
[21]	Mooney KH, Beck SL, Wong B, et al. Automated home monitoring and management of patient-reported symptoms during chemotherapy:results of the symptom care at home RCT[J]. Cancer Med, 2017, 6(3):537-546. DOI URL
[22]	Zhang LY, Zhang XT, Shen L, et al. Efficiency of electronic health record assessment of patient-reported outcomes after cancer immunotherapy:a randomized clinical trial[J]. JAMA Netw Open, 2022, 5(3):e224427. DOI URL
[23]	于蒙, 何文涛, 周绪川, 等. 推荐系统综述[J]. 计算机应用, 2022, 42(6):1898-1913. DOI
	Yu M, He WT, Zhou XC, et al. Review of recommendation system[J]. J Comput Appl, 2022, 42(6):1898-1913. DOI
[24]	冷敏敏, 赵雅洁, 李立玉, 等. 网络干预在痴呆老年人居家照护中应用的范围综述[J]. 中华护理杂志, 2021, 56(5):694-699. DOI URL
	Leng MM, Zhao YJ, Li LY, et al. Internet-based interventions for family care of older people with dementia:a scoping review[J]. Chin J Nurs, 2021, 56(5):694-699.
[25]	梁秀波, 吴俊涵, 赵昱, 等. 区块链数据安全管理和隐私保护技术研究综述[J]. 浙江大学学报(工学版), 2022, 56(1):1-15.
	Liang XB, Wu JH, Zhao Y, et al. Review of blockchain data security management and privacy protection technology research[J]. J Zhejiang Univ Eng Sci, 2022, 56(1):1-15.
[26]	Rani S, Kaur M, Kumar M. Recommender system:prediction/diagnosis of breast cancer using hybrid machine learning algorithm[J]. Multimed Tools Appl, 2022, 81(7):9939-9948. DOI
[27]	邓远志. 基于多模态心血管疾病数据的医疗推荐系统的设计与实现[D]. 广州: 华南理工大学, 2021.
	Deng YZ. Design and implementation of medical recommendation system based on multimodal cardiovascular disease data[D]. Guangzhou: South China University of Technology, 2021.
[28]	曲超然, 王青, 韩琳, 等. 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. DOI URL
	Qu CR, Wang Q, Han L, et al. A literature review on the application of machine learning algorithms in pressure injury management[J]. Chin J Nurs, 2021, 56(2):212-217. DOI URL
[29]	陆杰华, 韦晓丹. 老年数字鸿沟治理的分析框架、理念及其路径选择:基于数字鸿沟与知沟理论视角[J]. 人口研究, 2021, 45(3):17-30.
	Lu JH, Wei XD. Analysis framework,concept,and pathways of digital divide governance for older adults:from the perspec-tive of digital divide and knowledge gap theory[J]. Popul Res, 2021, 45(3):17-30.