Holle-Lee Dagny
Klinik und Poliklinik für Neurologie, Universitätsklinikum Essen, Hufelandstraße 55, 45147, Essen, Germany.
MMW Fortschr Med. 2024 Nov;166(20):67-69. doi: 10.1007/s15006-024-4435-9.
In recent years, machine learning, particularly Natural Language Processing, has emerged as a valuable tool for analyzing unstructured health data, such as headache anamneses. Studies demonstrate that algorithms can identify specific patterns and automate headache diagnoses. Various studies have also shown that machine learning algorithms achieve high diagnostic accuracy in distinguishing between headache types, including migraine and cluster headaches. Additionally, machine learning models are being used to predict potential triggers, treatment responses, and even the progression of headache disorders. Digital headache diaries and trigger-analysis apps are becoming increasingly important in therapy. Through digital health applications, new scalable non-drug options for headache and migraine therapy are available.
近年来,机器学习,尤其是自然语言处理,已成为分析非结构化健康数据(如头痛病史)的宝贵工具。研究表明,算法可以识别特定模式并自动进行头痛诊断。多项研究还表明,机器学习算法在区分头痛类型(包括偏头痛和丛集性头痛)方面具有很高的诊断准确性。此外,机器学习模型正被用于预测潜在的触发因素、治疗反应,甚至头痛疾病的进展。数字头痛日记和触发因素分析应用程序在治疗中变得越来越重要。通过数字健康应用程序,出现了用于头痛和偏头痛治疗的新的可扩展非药物选择。