Disseminating Natural Language Processed Clinical Narratives

Through Natural Language Processing (NLP) techniques, information can be extracted from clinical narratives for a variety of applications (e.g., patient management). While the complex and nested output of NLP systems can be expressed in standard formats, such as the eXtensible Markup Language (XML), these representations may not be directly suitable for certain end-users or applications. The availability of a âeuro tabular' format that simplifies the content and structure of NLP output may facilitate the dissemination and use by users who are more familiar with common spreadsheet, database, or statistical tools. In this paper, we describe the knowledge-based design of a tabular representation for NLP output and development of a transformation program for the structured output of MedLEE, an NLP system at our institution. Through an evaluation, we found that the simplified tabular format is comparable to existing more complex NLP formats in effectiveness for identifying clinical conditions in narrative reports.