University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Groningen, the Netherlands.
Institute of Bioinformatics, University Medicine Greifswald, Greifswald, Germany.
Clin Microbiol Infect. 2020 Oct;26(10):1291-1299. doi: 10.1016/j.cmi.2020.02.003. Epub 2020 Feb 13.
Machine learning (ML) is increasingly being used in many areas of health care. Its use in infection management is catching up as identified in a recent review in this journal. We present here a complementary review to this work.
To support clinicians and researchers in navigating through the methodological aspects of ML approaches in the field of infection management.
A Medline search was performed with the keywords artificial intelligence, machine learning, infection∗, and infectious disease∗ for the years 2014-2019. Studies using routinely available electronic hospital record data from an inpatient setting with a focus on bacterial and fungal infections were included.
Fifty-two studies were included and divided into six groups based on their focus. These studies covered detection/prediction of sepsis (n = 19), hospital-acquired infections (n = 11), surgical site infections and other postoperative infections (n = 11), microbiological test results (n = 4), infections in general (n = 2), musculoskeletal infections (n = 2), and other topics (urinary tract infections, deep fungal infections, antimicrobial prescriptions; n = 1 each). In total, 35 different ML techniques were used. Logistic regression was applied in 18 studies followed by random forest, support vector machines, and artificial neural networks in 18, 12, and seven studies, respectively. Overall, the studies were very heterogeneous in their approach and their reporting. Detailed information on data handling and software code was often missing. Validation on new datasets and/or in other institutions was rarely done. Clinical studies on the impact of ML in infection management were lacking.
Promising approaches for ML use in infectious diseases were identified. But building trust in these new technologies will require improved reporting. Explainability and interpretability of the models used were rarely addressed and should be further explored. Independent model validation and clinical studies evaluating the added value of ML approaches are needed.
机器学习（ML）在医疗保健的许多领域中得到了越来越多的应用。正如本刊最近的一篇综述中所指出的，它在感染管理中的应用也在逐渐普及。我们在此呈现该工作的补充综述。
帮助临床医生和研究人员在感染管理领域的 ML 方法的方法学方面进行导航。
使用“人工智能”、“机器学习”、“感染*”和“传染病*”等关键词在 Medline 上进行了 2014 年至 2019 年的搜索。纳入了使用常规可获得的住院患者电子病历数据且重点关注细菌和真菌感染的研究。
纳入了 52 项研究，并根据其重点分为六个组。这些研究涵盖了脓毒症（n=19）、医院获得性感染（n=11）、手术部位感染和其他术后感染（n=11）、微生物学检测结果（n=4）、一般感染（n=2）、肌肉骨骼感染（n=2）和其他主题（尿路感染、深部真菌感染、抗菌药物处方；n=1 项）的检测/预测。总共使用了 35 种不同的 ML 技术。18 项研究中应用了逻辑回归，18 项、12 项和 7 项研究中分别应用了随机森林、支持向量机和人工神经网络。总的来说，这些研究在方法和报告方面非常具有异质性。关于数据处理和软件代码的详细信息通常缺失。很少在新数据集或其他机构中进行验证。缺乏关于 ML 在感染管理中的应用的临床研究。
确定了在传染病中使用 ML 的有前景的方法。但是，要建立对这些新技术的信任，需要改进报告。模型的可解释性和可解释性很少被提及，应进一步探索。需要独立的模型验证和评估 ML 方法附加值的临床研究。
