As automated data extraction and natural language processing (NLP) are rapidly evolving, applicability to harness large data to improve healthcare delivery is garnering great interest. Assessing antiepileptic drug (AED) efficacy remains a barrier to improving epilepsy care. In this review, we examined automatic electronic health record (EHR) extraction methodologies pertinent to epilepsy examining AED efficacy. We also reviewed more generalizable NLP pipelines to extract other critical patient variables. Our review found varying reports of performance measures. Whereas automated data extraction pipelines are a crucial advancement, this review calls attention to standardizing NLP methodology and accuracy reporting for greater generalizability. Moreover, the use of crowdsourcing competitions to spur innovative NLP pipelines would further advance this field.

中文翻译：

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是否可以从电子健康记录中研究抗癫痫药的功效？回顾当前的方法

随着自动数据提取和自然语言处理（NLP）的迅速发展，利用大数据来改善医疗保健的适用性引起了人们的极大兴趣。评估抗癫痫药（AED）的疗效仍然是改善癫痫治疗的障碍。在这篇综述中，我们研究了与癫痫有关的自动电子健康记录（EHR）提取方法，研究了AED的功效。我们还回顾了更具通用性的NLP管道，以提取其他重要的患者变量。我们的审查发现了不同的绩效指标报告。尽管自动化数据提取管道是一项至关重要的进步，但本次审查呼吁人们关注标准化NLP方法和准确性报告，以实现更高的通用性。此外，