Barghouthi Eba'a Dasan, Owda Amani Yousef, Asia Mohammad, Owda Majdi
Health Sciences Department, Arab American University, Ramallah P600, Palestine.
Department of Natural Engineering and Technology Sciences, Arab American University, Ramallah P600, Palestine.
Diagnostics (Basel). 2023 Aug 23;13(17):2739. doi: 10.3390/diagnostics13172739.
Pressure injuries are increasing worldwide, and there has been no significant improvement in preventing them. This study is aimed at reviewing and evaluating the studies related to the prediction model to identify the risks of pressure injuries in adult hospitalized patients using machine learning algorithms. In addition, it provides evidence that the prediction models identified the risks of pressure injuries earlier. The systematic review has been utilized to review the articles that discussed constructing a prediction model of pressure injuries using machine learning in hospitalized adult patients. The search was conducted in the databases Cumulative Index to Nursing and Allied Health Literature (CINAHIL), PubMed, Science Direct, the Institute of Electrical and Electronics Engineers (IEEE), Cochrane, and Google Scholar. The inclusion criteria included studies constructing a prediction model for adult hospitalized patients. Twenty-seven articles were included in the study. The defects in the current method of identifying risks of pressure injury led health scientists and nursing leaders to look for a new methodology that helps identify all risk factors and predict pressure injury earlier, before the skin changes or harms the patients. The paper critically analyzes the current prediction models and guides future directions and motivations.
全球范围内压力性损伤的发生率正在上升，且在预防方面没有显著改善。本研究旨在回顾和评估与预测模型相关的研究，以使用机器学习算法识别成年住院患者发生压力性损伤的风险。此外，它还提供了证据表明预测模型能够更早地识别压力性损伤的风险。本系统综述被用于回顾那些讨论在成年住院患者中使用机器学习构建压力性损伤预测模型的文章。检索在护理及相关健康文献累积索引数据库（CINAHL）、PubMed、科学Direct、电气和电子工程师协会（IEEE）、Cochrane以及谷歌学术中进行。纳入标准包括为成年住院患者构建预测模型的研究。该研究共纳入27篇文章。当前识别压力性损伤风险方法的缺陷促使健康科学家和护理领导者去寻找一种新方法，该方法有助于在皮肤发生变化或对患者造成伤害之前，识别所有风险因素并更早地预测压力性损伤。本文对当前的预测模型进行了批判性分析，并为未来的方向和动机提供了指导。
