背景:迄今为止,没有关于机器学习(ML)在烧伤护理中的评论。考虑到ML在医学中的增长以及烧伤护理的复杂性和挑战,这篇综述主要介绍ML在烧伤护理中的应用。目的是研究针对烧伤护理和研究各个方面的应用特征和影响。
方法:MEDLINE,Cochrane系统评价数据库,ScienceDirect,并检索了相关主要文章和综述文章的引文综述,寻找涉及烧伤护理/研究和机器学习的研究.数据在研究设计上进行了抽象,研究规模,Year,人口,烧伤护理/研究的应用,ML技术,和算法性能。
结果:15项涉及烧伤患者的回顾性观察研究符合纳入标准。在5105例急性热损伤患者中,171临床烧伤伤口,180个9聚体肽,研究中包括424种12聚体肽。研究集中在烧伤诊断(n=5),氨基糖苷类反应(n=3),住院时间(n=2),生存率/死亡率(n=4),烧伤愈合时间(n=1),烧伤患者的抗菌肽(n=1)。在这15项研究中,11使用人工神经网络。重要的是,所有研究都证明了ML在烧伤护理/研究中的益处,并且优于传统统计方法.然而,不同的作者对算法性能进行了不同的评估。特征选择因研究而异,但是具有类似应用程序的研究具有特定的特征,包括年龄,性别,存在吸入性损伤,全身表面积烧伤,如果有的话,不同程度的烧伤,感染,和烧伤患者以前的病史/状况。
结论:可以确定烧伤治疗/研究中ML的共同特征基础,但ML的影响将需要在前瞻性观察研究和随机临床试验中进一步验证,建立通用的性能指标,以及关于临床和经济影响的高质量证据。只有这样,ML应用才能在烧伤护理/研究中得到改进和广泛接受。
BACKGROUND: To date, there are no reviews on machine learning (ML) in burn care. Considering the growth of ML in medicine and the complexities and challenges of burn care, this
review specializes on ML applications in burn care. The objective was to examine the features and impact of applications in targeting various aspects of burn care and research.
METHODS: MEDLINE, the Cochrane Database of Systematic Reviews, ScienceDirect, and citation
review of relevant primary and
review articles were searched for studies involving burn care/research and machine learning. Data were abstracted on study design, study size, year, population, application of burn care/research, ML technique(s), and algorithm performance.
RESULTS: 15 retrospective observational studies involving burn patients met inclusion criteria. In total 5105 patients with acute thermal injury, 171 clinical burn wounds, 180 9-mer peptides, and 424 12-mer peptides were included in the studies. Studies focused on burn diagnosis (n=5), aminoglycoside response (n=3), hospital length of stay (n=2), survival/mortality (n=4), burn healing time (n=1), and antimicrobial peptides in burn patients (n=1). Of these 15 studies, 11 used artificial neural networks. Importantly, all studies demonstrated the benefits of ML in burn care/research and superior performance over traditional statistical methods. However, algorithm performance was assessed differently by different authors. Feature selection varied among studies, but studies with similar applications shared specific features including age, gender, presence of inhalation injury, total body surface area burned, and when available, various degrees of burns, infections, and previous histories/conditions of burn patients.
CONCLUSIONS: A common feature base may be determined for ML in burn care/research, but the impact of ML will require further validation in prospective observational studies and randomized clinical trials, establishment of common performance metrics, and high quality evidence about clinical and economic impacts. Only then can ML applications be advanced and accepted widely in burn care/research.