Authors:
Eddie Paul Hernández
1
;
Alexandra Pomares Quimbaya
1
and
Oscar Mauricio Muñoz
2
Affiliations:
1
Pontificia Universidad Javeriana, Colombia
;
2
Pontificia Universidad Javeriana and Hospital Universitario San Ignacio, Colombia
Keyword(s):
Text Mining, Clinical Practice Guidelines, Medical Events, Temporal Expressions, Electronic Health Records.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Health Engineering and Technology Applications
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
Electronic health records contain important information of a patient and it may serve as source to analyze and audit the process of diagnosis and treatment of a specific clinical condition. This information is registered in narrative text, which generates a limitation to identify medical events like doctor appointments, medications, treatments, surgical procedures, etc. As it is difficult to identify medical events in electronic health records, it is not easy to find a point of comparison between this electronic information with recommendations given by clinical practice guidelines. Such guides correspond to recommendations systematically developed to assist health professionals in taking appropriate decisions with respect to illness. This article presents “Health Text Line Model HTL”, a model for extraction, structuring and viewing medical events from narrative text in electronic health records. The HTL model was implemented in a framework that integrates the aforementioned processe
s to identify and timing medical events. HTL was validated in a general hospital giving good results on precision and recall.
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