Medical events extraction to analyze clinical records with conditional random fields

Issue title: Special Section: Intelligent and Fuzzy Systems applied to Language & Knowledge Engineering

Guest editors: David Pinto and Vivek Singh

Article type: Research Article

Authors: Fócil-Arias, Carolina | Sidorov, Grigori^{; *} | Gelbukh, Alexander

Affiliations: Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico

Correspondence: [*] Corresponding author. G. Sidorov, Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico. E-mail: [email protected].

Abstract: The rapid growth in the extraction of clinical events from unstructured clinical records has raised considerable challenges. In this paper, we propose the use of different features with a statical modeling method called conditional random fields, which is consider an algorithm for effectively solving problems of sequence tagging. Our goal is to determine which feature selection can affect the performance of four subtasks presented in SemEval Task-12: Clinical TempEval 2016. We applied a careful preprocessing, where the proposed method was tested on real clinical records from Task-12: Clinical TempEval 2016. The comparative analyses obtained indicate that our proposal achieves good results compared to the work presented in Task-12: Clinical TempEval 2016 challenges.

Keywords: Clinical reports, medical information extraction, natural language processing, machine learning, feature selection, conditional random fields

DOI: 10.3233/JIFS-179014

Journal: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4633-4643, 2019