Department of Clinical Pharmacology, Royal Adelaide Hospital, Adelaide, South Australia, Australia.
Adelaide Medical School, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, Australia.
Intern Med J. 2023 Aug;53(8):1485-1488. doi: 10.1111/imj.16194.
There is a growing interest in the appropriate evaluation of penicillin adverse drug reaction (ADR) labels. We have developed machine learning models for classifying penicillin ADR labels using free-text reaction descriptions, and here report external and practical validation. The models performed comparably with expert criteria for the categorisation of allergy or intolerance and identification of high-risk allergies. These models have practical applications in detecting individuals suitable for penicillin ADR evaluation. Implementation studies are required.
人们越来越关注对青霉素不良反应(ADR)标签的适当评估。我们已经开发了使用自由文本反应描述对青霉素 ADR 标签进行分类的机器学习模型,并在此报告外部和实际验证。这些模型在过敏或不耐受的分类以及高风险过敏的识别方面与专家标准表现相当。这些模型在检测适合青霉素 ADR 评估的个体方面具有实际应用。需要进行实施研究。