Department of Biomedical Informatics, University of Pittsburgh, Pennsylvania.
Center for Evolutionary Biology and Medicine, University of Pittsburgh, Pennsylvania.
J Infect Dis. 2024 Nov 15;230(5):1073-1082. doi: 10.1093/infdis/jiae348.
There is growing excitement about the clinical use of artificial intelligence and machine learning (ML) technologies. Advancements in computing and the accessibility of ML frameworks enable researchers to easily train predictive models using electronic health record data. However, several practical factors must be considered when employing ML on electronic health record data. We provide a primer on ML and approaches commonly taken to address these challenges. To illustrate how these approaches have been applied to address antimicrobial resistance, we review the use of electronic health record data to construct ML models for predicting pathogen carriage or infection, optimizing empiric therapy, and aiding antimicrobial stewardship tasks. ML shows promise in promoting the appropriate use of antimicrobials, although clinical deployment is limited. We conclude by describing the potential dangers of, and barriers to, implementation of ML models in the clinic.
人们对人工智能和机器学习（ML）技术的临床应用越来越感到兴奋。计算能力的提高和 ML 框架的普及使研究人员能够轻松地使用电子健康记录数据训练预测模型。然而，在使用电子健康记录数据时，必须考虑几个实际因素。我们提供了一个关于 ML 以及常见方法的入门指南，以解决这些挑战。为了说明这些方法如何应用于解决抗菌素耐药性问题，我们回顾了使用电子健康记录数据构建用于预测病原体携带或感染、优化经验性治疗以及辅助抗菌药物管理任务的 ML 模型的方法。ML 在促进抗菌药物的合理使用方面显示出了巨大的潜力，尽管临床应用仍受到限制。最后，我们描述了在临床中实施 ML 模型的潜在危险和障碍。
