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    <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.</abstract>
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%0 Conference Proceedings
%T Understanding Medical free text: A Terminology driven approach
%A Krishna, Santosh Sai
%A Hans, Manoj
%Y Drouin, Patrick
%Y Grabar, Natalia
%Y Hamon, Thierry
%Y Kageura, Kyo
%Y Takeuchi, Koichi
%S Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F krishna-hans-2016-understanding
%X 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.
%U https://aclanthology.org/W16-4714
%P 121-125

Markdown (Informal)

[Understanding Medical free text: A Terminology driven approach](https://aclanthology.org/W16-4714) (Krishna & Hans, CompuTerm 2016)

• Understanding Medical free text: A Terminology driven approach (Krishna & Hans, CompuTerm 2016)

ACL

• Santosh Sai Krishna and Manoj Hans. 2016. Understanding Medical free text: A Terminology driven approach. In Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016), pages 121–125, Osaka, Japan. The COLING 2016 Organizing Committee.