A Method to Detect Errors in Electronic Discharge Summaries Based on Named Entity Recognition

DOI

10.2991/aiie-15.2015.8How to use a DOI?

Keywords

named entity recognition; natural language processing; electronic discharge summary; error detectio; conditional random field

Abstract

The hospital discharge summaryis an essential document, containing clinical and administrative information necessary for the continuity of care after the patients are discharged from hospital. The utilization of electronic discharge summaries has grown in popularity. However, many transcription errors and spelling mistakes exit, potentially reducing the medical quality of patient care. To solve this problem, this paper presents a novel approach to detect these errors automatically by using Named entity recognition (NER). The NER model was trained by 450 discharge summaries and rich features set was used to improve the recall and precision. Experiment on the independent test set validated the good performance of NER. The follow-up error detection using the trained NER discovered that the mistakes and ambiguous information that frequently occurred in discharge summaries.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - D.S. Yuan
AU  - T.S. Zhou
AU  - Y. Tian
AU  - J.S. Li
PY  - 2015/07
DA  - 2015/07
TI  - A Method to Detect Errors in Electronic Discharge Summaries Based on Named Entity Recognition
BT  - Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering
PB  - Atlantis Press
SP  - 25
EP  - 28
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-15.2015.8
DO  - 10.2991/aiie-15.2015.8
ID  - Yuan2015/07
ER  -