期刊
JOURNAL OF CARDIOVASCULAR TRANSLATIONAL RESEARCH
卷 10, 期 3, 页码 313-321出版社
SPRINGER
DOI: 10.1007/s12265-017-9752-2
关键词
Information extraction; Natural language processing; Clinical trials; Precision medicine
资金
- National Library of Medicine [R00LM011389, R01LM011416]
- Novartis
- National Institutes of Health [R01 HL107577, R01 HL127028]
Precision medicine requires clinical trials that are able to efficiently enroll subtypes of patients in whom targeted therapies can be tested. To reduce the large amount of time spent screening, identifying, and recruiting patients with specific subtypes of heterogeneous clinical syndromes (such as heart failure with preserved ejection fraction [HFpEF]), we need prescreening systems that are able to automate data extraction and decision-making tasks. However, a major obstacle is the vast amount of unstructured free-form text in medical records. Here we describe an information extraction-based approach that automatically converts unstructured text into structured data, which is cross-referenced against eligibility criteria using a rule-based system to determine which patients qualify for a major HFpEF clinical trial (PARAGON). We show that we can achieve a sensitivity and positive predictive value of 0.95 and 0.86, respectively. Our open-source algorithm could be used to efficiently identify and subphenotype patients with HFpEF and other disorders.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据