Zou Xiantong, Liu Yingning, Ji Linong
Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China.
Digit Health. 2023 Sep 29;9:20552076231203879. doi: 10.1177/20552076231203879. eCollection 2023 Jan-Dec.
Precision pharmacotherapy of diabetes requires judicious selection of the optimal therapeutic agent for individual patients. Artificial intelligence (AI), a swiftly expanding discipline, holds substantial potential to transform current practices in diabetes diagnosis and management. This manuscript provides a comprehensive review of contemporary research investigating drug responses in patient subgroups, stratified via either supervised or unsupervised machine learning approaches. The prevalent algorithmic workflow for investigating drug responses using machine learning involves cohort selection, data processing, predictor selection, development and validation of machine learning methods, subgroup allocation, and subsequent analysis of drug response. Despite the promising feature, current research does not yet provide sufficient evidence to implement machine learning algorithms into routine clinical practice, due to a lack of simplicity, validation, or demonstrated efficacy. Nevertheless, we anticipate that the evolving evidence base will increasingly substantiate the role of machine learning in molding precision pharmacotherapy for diabetes.
糖尿病的精准药物治疗需要为个体患者明智地选择最佳治疗药物。人工智能（AI）作为一个迅速发展的学科，在改变当前糖尿病诊断和管理实践方面具有巨大潜力。本文对当代研究进行了全面综述，这些研究通过监督或无监督机器学习方法对患者亚组的药物反应进行了调查。使用机器学习研究药物反应的常见算法工作流程包括队列选择、数据处理、预测变量选择、机器学习方法的开发和验证、亚组分配以及随后的药物反应分析。尽管有前景，但由于缺乏简单性、验证或已证实的疗效，目前的研究尚未提供足够的证据将机器学习算法应用于常规临床实践。然而，我们预计不断发展的证据基础将越来越多地证实机器学习在塑造糖尿病精准药物治疗中的作用。
