Using Machine Learning Technologies in Pressure Injury Management: Systematic Review.
Using Machine Learning Technologies in Pressure Injury Management: Systematic Review.
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DOI：
10.2196/25704
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发表时间：
2021-03-10
影响因子：
3.2
通讯作者：
An N
中科院分区：
文献类型：
--
作者：
Jiang M;Ma Y;Guo S;Jin L;Lv L;Han L;An N
压力性损伤（PI）是一个常见且可预防的问题，但由于至少两个原因它仍是一项挑战。首先，护士短缺是一个全球现象。其次，大多数护士缺乏与压力性损伤相关的知识。机器学习（ML）技术可通过提高压力性损伤的预后和诊断准确性来减轻医护人员的负担。据我们所知，目前尚无系统综述评估当前机器学习技术在压力性损伤管理中的应用情况。
本综述的目的是综合和评估有关机器学习技术在压力性损伤管理中应用的文献，确定其优势和劣势，并为未来的研究和实践找出改进的机会。
我们在PubMed、Embase、Web of Science、护理及相关健康文献累积索引（CINAHL）、Cochrane图书馆、中国知网（CNKI）、万方数据库、维普数据库和中国生物医学文献数据库（CBM）上进行了广泛搜索以确定相关文章。搜索于2020年6月进行。两名独立的研究人员进行了研究选择、数据提取和质量评估。使用预测模型偏倚风险评估工具（PROBAST）评估偏倚风险。
共有32篇文章符合纳入标准。其中12篇文章（38%）报告使用机器学习技术开发预测模型以识别风险因素，11篇（34%）报告将其用于体位检测和识别，9篇（28%）报告将其用于压力性损伤伤口的组织分类和测量的图像分析。这些文章介绍了各种算法并测量了结果。总体偏倚风险被判定为高。
有一系列新兴的机器学习技术被用于压力性损伤管理，它们在实验室中的结果显示出很大的潜力。未来的研究应使用临床数据大规模应用这些技术，以进一步验证和提高其有效性，并提高方法学质量。
Pressure injury (PI) is a common and preventable problem, yet it is a challenge for at least two reasons. First, the nurse shortage is a worldwide phenomenon. Second, the majority of nurses have insufficient PI-related knowledge. Machine learning (ML) technologies can contribute to lessening the burden on medical staff by improving the prognosis and diagnostic accuracy of PI. To the best of our knowledge, there is no existing systematic review that evaluates how the current ML technologies are being used in PI management. The objective of this review was to synthesize and evaluate the literature regarding the use of ML technologies in PI management, and identify their strengths and weaknesses, as well as to identify improvement opportunities for future research and practice. We conducted an extensive search on PubMed, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, China National Knowledge Infrastructure (CNKI), the Wanfang database, the VIP database, and the China Biomedical Literature Database (CBM) to identify relevant articles. Searches were performed in June 2020. Two independent investigators conducted study selection, data extraction, and quality appraisal. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). A total of 32 articles met the inclusion criteria. Twelve of those articles (38%) reported using ML technologies to develop predictive models to identify risk factors, 11 (34%) reported using them in posture detection and recognition, and 9 (28%) reported using them in image analysis for tissue classification and measurement of PI wounds. These articles presented various algorithms and measured outcomes. The overall risk of bias was judged as high. There is an array of emerging ML technologies being used in PI management, and their results in the laboratory show great promise. Future research should apply these technologies on a large scale with clinical data to further verify and improve their effectiveness, as well as to improve the methodological quality.
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影响因子：
4.5
作者：
An, Ning;Ding, Huitong;Yang, Jiaoyun;Au, Rhoda;Ang, Ting F. A.
通讯作者：
Ang, Ting F. A.
影响因子：
--
作者：
Kosmopoulos, Dimitrios I.;Tzevelekou, Fotini L.
通讯作者：
Tzevelekou, Fotini L.
影响因子：
4.2
作者：
Li, Dan;Mathews, Carol
通讯作者：
Mathews, Carol
影响因子：
3.8
作者：
Capon, Alessandra;Pavoni, Noemi;Di Lallo, Domenico
通讯作者：
Di Lallo, Domenico
影响因子：
3.2
作者：
Garcia-Zapirain, Begona;Elmogy, Mohammed;Elmaghraby, Adel S.
通讯作者：
Elmaghraby, Adel S.