A Novel System for Extractive Clinical Note Summarization using EHR Data

Jennifer Liang, Ching-Huei Tsou, Ananya Poddar


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.
Anthology ID:
W19-1906
Original:
W19-1906v1
Version 2:
W19-1906v2
Volume:
Proceedings of the 2nd Clinical Natural Language Processing Workshop
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Editors:
Anna Rumshisky, Kirk Roberts, Steven Bethard, Tristan Naumann
Venue:
ClinicalNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
46–54
Language:
URL:
https://aclanthology.org/W19-1906
DOI:
10.18653/v1/W19-1906
Bibkey:
Cite (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.
Cite (Informal):
A Novel System for Extractive Clinical Note Summarization using EHR Data (Liang et al., ClinicalNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-1906.pdf