National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK; French Institute for Medical Research (Inserm), Infection Antimicrobials Modelling Evolution (IAME), UMR 1137, University Paris Diderot, Paris, France.
National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK.
Clin Microbiol Infect. 2020 May;26(5):584-595. doi: 10.1016/j.cmi.2019.09.009. Epub 2019 Sep 17.
Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID).
We aim to inform clinicians about the use of ML for diagnosis, classification, outcome prediction and antimicrobial management in ID.
References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, ACM Digital Library, arXiV and IEEE Xplore Digital Library up to July 2019.
We found 60 unique ML-clinical decision support systems (ML-CDSS) aiming to assist ID clinicians. Overall, 37 (62%) focused on bacterial infections, 10 (17%) on viral infections, nine (15%) on tuberculosis and four (7%) on any kind of infection. Among them, 20 (33%) addressed the diagnosis of infection, 18 (30%) the prediction, early detection or stratification of sepsis, 13 (22%) the prediction of treatment response, four (7%) the prediction of antibiotic resistance, three (5%) the choice of antibiotic regimen and two (3%) the choice of a combination antiretroviral therapy. The ML-CDSS were developed for intensive care units (n = 24, 40%), ID consultation (n = 15, 25%), medical or surgical wards (n = 13, 20%), emergency department (n = 4, 7%), primary care (n = 3, 5%) and antimicrobial stewardship (n = 1, 2%). Fifty-three ML-CDSS (88%) were developed using data from high-income countries and seven (12%) with data from low- and middle-income countries (LMIC). The evaluation of ML-CDSS was limited to measures of performance (e.g. sensitivity, specificity) for 57 ML-CDSS (95%) and included data in clinical practice for three (5%).
Considering comprehensive patient data from socioeconomically diverse healthcare settings, including primary care and LMICs, may improve the ability of ML-CDSS to suggest decisions adapted to various clinical contexts. Currents gaps identified in the evaluation of ML-CDSS must also be addressed in order to know the potential impact of such tools for clinicians and patients.
机器学习（ML）是医学领域的一个新兴领域。本叙述性综述描述了目前关于 ML 用于传染病（ID）临床决策支持的文献。
我们旨在为临床医生提供有关 ML 在 ID 中的诊断、分类、结果预测和抗菌管理中的使用信息。
本综述的参考文献是通过搜索 MEDLINE/PubMed、EMBASE、Google Scholar、biorXiv、ACM 数字图书馆、arXiV 和 IEEE Xplore 数字图书馆获得的，截至 2019 年 7 月。
我们发现了 60 个独特的 ML-临床决策支持系统（ML-CDSS），旨在帮助 ID 临床医生。总体而言，37 个（62%）专注于细菌感染，10 个（17%）专注于病毒感染，9 个（15%）专注于结核病，4 个（7%）专注于任何类型的感染。其中，20 个（33%）解决了感染的诊断，18 个（30%）解决了脓毒症的早期检测或分层，13 个（22%）解决了治疗反应的预测，4 个（7%）解决了抗生素耐药性的预测，3 个（5%）解决了抗生素方案的选择，2 个（3%）解决了联合抗逆转录病毒疗法的选择。ML-CDSS 是为重症监护病房（n=24，40%）、ID 咨询（n=15，25%）、医疗或外科病房（n=13，20%）、急诊部（n=4，7%）、初级保健（n=3，5%）和抗菌药物管理（n=1，2%）开发的。53 个 ML-CDSS（88%）是使用高收入国家的数据开发的，7 个（12%）是使用中低收入国家（LMIC）的数据开发的。对 ML-CDSS 的评估仅限于性能测量（例如敏感性、特异性），其中 57 个 ML-CDSS（95%）进行了评估，并包括 3 个临床实践中的数据（5%）。
考虑到来自社会经济多样化医疗保健环境的综合患者数据，包括初级保健和 LMIC，可以提高 ML-CDSS 提出适应各种临床环境的决策的能力。目前，必须解决在 ML-CDSS 评估中发现的差距，以便了解这些工具对临床医生和患者的潜在影响。
