Understanding Medical free text: A Terminology driven approach

Santosh Sai Krishna, Manoj Hans


Abstract
With many hospitals digitalizing clinical records it has opened opportunities for researchers in NLP, Machine Learning to apply techniques for extracting meaning and make actionable insights. There has been previous attempts in mapping free text to medical nomenclature like UMLS, SNOMED. However, in this paper, we had analyzed diagnosis in clinical reports using ICD10 to achieve a lightweight, real-time predictions by introducing concepts like WordInfo, root word identification. We were able to achieve 68.3% accuracy over clinical records collected from qualified clinicians. Our study would further help the healthcare institutes in organizing their clinical reports based on ICD10 mappings and derive numerous insights to achieve operational efficiency and better medical care.
Anthology ID:
W16-4714
Volume:
Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)
Month:
December
Year:
2016
Address:
Osaka, Japan
Venues:
CompuTerm | WS
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
121–125
Language:
URL:
https://www.aclweb.org/anthology/W16-4714
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/W16-4714.pdf