Extraction of temporal relations from clinical free text: A systematic review of current approaches.

Journal: Journal of biomedical informatics

Published Date: Jul 13, 2020

Abstract

BACKGROUND: Temporal relations between clinical events play an important role in clinical assessment and decision making. Extracting such relations from free text data is a challenging task because it lies on between medical natural language processing, temporal representation and temporal reasoning.

Authors

Ghada Alfattni

Department of Computer Science, University of Manchester, Manchester, UK; Department of Computer Science, Jamoum University College, Umm Al-Qura University, Makkah, Saudi Arabia. Electronic address: gafattni@uqu.edu.sa.
Niels Peek

Health e-Research Centre, University of Manchester, Vaughan House, Portsmouth Street, Manchester M13 9GB, UK. Electronic address: niels.peek@manchester.ac.uk.
Goran Nenadic

School of Computer Science, University of Manchester, Manchester, UK.

Keywords

Data Mining Electronic Health Records Information Storage and Retrieval Machine Learning Natural Language Processing Time

External Resources

View on PubMed Access via DOI PubMed (32673788)

Extraction of temporal relations from clinical free text: A systematic review of current approaches.

Abstract

Authors

Keywords

External Resources

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