Book ChapterDOI
Towards Cross Language Morphologic Negation Identification in Electronic Health Records
Ioana Barbantan,Rodica Potolea +1 more
- pp 417-430
TLDR
An approach for analyzing the Electronic Health Records (EHRs) with the goal of automatically identifying morphologic negation such that swapping the truth values of concepts introduced by negation does not interfere with understanding the medical discourse is presented.Abstract:
The current paper presents an approach for analyzing the Electronic Health Records (EHRs) with the goal of automatically identifying morphologic negation such that swapping the truth values of concepts introduced by negation does not interfere with understanding the medical discourse. To identify morphologic negation we propose the RoPreNex strategy that represents the adaptation of our PreNex approach to the Romanian language [1]. We evaluate our proposed solution on the MTsamples [2] dataset. The results we obtained are promising and ensure a reliable negation identification approach in medical documents. We report precision of 92.62 % and recall of 93.60 % in case of the morphologic negation identification for the source language and an overall performance in the morphologic negation identification of 77.78 % precision and 80.77 % recall in case of the target language.read more
Citations
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Journal ArticleDOI
Feature Engineered Relation Extraction – Medical Documents Setting
TL;DR: This paper aims to define the knowledge flow for a medical assistive decision support system by structuring raw medical data and leveraging the knowledge contained in the data proposing solutions for efficient data search, medical investigation or diagnosis and medication prediction and relationship identification.
Knowledge Extraction and Prediction from Unstructured Medical Documents
Ioana Barbantan,Rodica Potolea +1 more
TL;DR: This paper covers an original complete solution for automatically structuring medical documents and extracting relevant medical concepts via the PreNex and MedCIM strategies while the vision for the Knowledge Extraction and Prediction solutions is being argued and is under development.
References
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