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        <title>A Novel System for Extractive Clinical Note Summarization using EHR Data</title>
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        <namePart type="given">Jennifer</namePart>
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    <abstract>While much data within a patient’s electronic health record (EHR) is coded, crucial information concerning the patient’s care and management remain buried in unstructured clinical notes, making it difficult and time-consuming for physicians to review during their usual clinical workflow. In this paper, we present our clinical note processing pipeline, which extends beyond basic medical natural language processing (NLP) with concept recognition and relation detection to also include components specific to EHR data, such as structured data associated with the encounter, sentence-level clinical aspects, and structures of the clinical notes. We report on the use of this pipeline in a disease-specific extractive text summarization task on clinical notes, focusing primarily on progress notes by physicians and nurse practitioners. We show how the addition of EHR-specific components to the pipeline resulted in an improvement in our overall system performance and discuss the potential impact of EHR-specific components on other higher-level clinical NLP tasks.</abstract>
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%0 Conference Proceedings
%T A Novel System for Extractive Clinical Note Summarization using EHR Data
%A Liang, Jennifer
%A Tsou, Ching-Huei
%A Poddar, Ananya
%Y Rumshisky, Anna
%Y Roberts, Kirk
%Y Bethard, Steven
%Y Naumann, Tristan
%S Proceedings of the 2nd Clinical Natural Language Processing Workshop
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F liang-etal-2019-novel-system
%X While much data within a patient’s electronic health record (EHR) is coded, crucial information concerning the patient’s care and management remain buried in unstructured clinical notes, making it difficult and time-consuming for physicians to review during their usual clinical workflow. In this paper, we present our clinical note processing pipeline, which extends beyond basic medical natural language processing (NLP) with concept recognition and relation detection to also include components specific to EHR data, such as structured data associated with the encounter, sentence-level clinical aspects, and structures of the clinical notes. We report on the use of this pipeline in a disease-specific extractive text summarization task on clinical notes, focusing primarily on progress notes by physicians and nurse practitioners. We show how the addition of EHR-specific components to the pipeline resulted in an improvement in our overall system performance and discuss the potential impact of EHR-specific components on other higher-level clinical NLP tasks.
%R 10.18653/v1/W19-1906
%U https://aclanthology.org/W19-1906
%U https://doi.org/10.18653/v1/W19-1906
%P 46-54

Markdown (Informal)

[A Novel System for Extractive Clinical Note Summarization using EHR Data](https://aclanthology.org/W19-1906) (Liang et al., ClinicalNLP 2019)

• A Novel System for Extractive Clinical Note Summarization using EHR Data (Liang et al., ClinicalNLP 2019)

ACL

• Jennifer Liang, Ching-Huei Tsou, and Ananya Poddar. 2019. A Novel System for Extractive Clinical Note Summarization using EHR Data. In Proceedings of the 2nd Clinical Natural Language Processing Workshop, pages 46–54, Minneapolis, Minnesota, USA. Association for Computational Linguistics.