使用临床记录的自然语言处理识别外周动脉疾病病例
Identifying Peripheral Arterial Disease Cases Using Natural Language Processing of Clinical Notes.
作者信息
Afzal Naveed, Sohn Sunghwan, Abram Sara, Liu Hongfang, Kullo Iftikhar J, Arruda-Olson Adelaide M
机构信息
Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester MN.
Division of Cardiovascular Diseases, Mayo Clinic, Rochester MN.
出版信息
IEEE EMBS Int Conf Biomed Health Inform. 2016 Feb;2016:126-131. doi: 10.1109/BHI.2016.7455851. Epub 2016 Apr 21.
Abstract
Peripheral arterial disease (PAD) is a chronic disease that affects millions of people worldwide. Ascertaining PAD status from clinical notes by manual chart review is labor intensive and time consuming. In this paper, we describe a natural language processing (NLP) algorithm for automated ascertainment of PAD status from clinical notes using predetermined criteria. We developed and evaluated our system against a gold standard that was created by medical experts based on manual chart review. Our system ascertained PAD status from clinical notes with high sensitivity (0.96), positive predictive value (0.92), negative predictive value (0.99) and specificity (0.98). NLP approaches can be used for rapid, efficient and automated ascertainment of PAD cases with implications for patient care and epidemiologic research.
摘要
外周动脉疾病（PAD）是一种影响全球数百万人的慢性疾病。通过人工查阅病历从临床记录中确定PAD状态既费力又耗时。在本文中，我们描述了一种自然语言处理（NLP）算法，用于根据预定标准从临床记录中自动确定PAD状态。我们根据医学专家通过人工查阅病历创建的金标准开发并评估了我们的系统。我们的系统从临床记录中确定PAD状态时具有高灵敏度（0.96）、阳性预测值（0.92）、阴性预测值（0.99）和特异性（0.98）。NLP方法可用于快速、高效且自动地确定PAD病例，这对患者护理和流行病学研究具有重要意义。
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