Van Vleck Tielman T, Elhadad Noémie
Department of Biomedical Informatics, Columbia University, New York, NY.
AMIA Annu Symp Proc. 2010 Nov 13;2010:817-21.
Physicians have access to patient notes in volumes far greater than what is practical to read within the context of a standard clinical scenario. As a preliminary step toward being able to provide a longitudinal summary of patient history, methods are examined for the automated extraction of relevant patient problems from existing clinical notes. We explore a grounded approach to identifying important patient problems from patient history. Methods build on existing NLP and text-summarization methodologies and leverage features observed in a relevant corpus.
医生能够获取的患者病历数量远远超过在标准临床场景下实际可阅读的量。作为能够提供患者病史纵向总结的初步步骤,我们研究了从现有临床病历中自动提取相关患者问题的方法。我们探索了一种基于实际情况的方法,从患者病史中识别重要的患者问题。这些方法基于现有的自然语言处理和文本摘要方法,并利用在相关语料库中观察到的特征。