2016
Syed Sibte Raza Abidi; Abhinav Kumar Singh; Sean Christie
Transcription of Case Report Forms from Unstructured Referral Letters: A Semantic Text Analytics Approach Proceedings Article
In: Exploring Complexity in Health: An Interdisciplinary Systems Approach. 26th European Medical Informatics Conference (MIE2016), Munich, pp. 322-326, IOS Press, 2016.
Abstract | Links | BibTeX | Tags: Referral letters, semantic mapping, Unstructured text analysis
@inproceedings{Abidi2016b,
title = {Transcription of Case Report Forms from Unstructured Referral Letters: A Semantic Text Analytics Approach},
author = {Syed Sibte Raza Abidi and Abhinav Kumar Singh and Sean Christie},
doi = {10.3233/978-1-61499-678-1-322},
year = {2016},
date = {2016-08-15},
booktitle = {Exploring Complexity in Health: An Interdisciplinary Systems Approach. 26th European Medical Informatics Conference (MIE2016), Munich},
volume = {228},
pages = {322-326},
publisher = {IOS Press},
series = {Studies in Health Technology and Informatics},
abstract = {In this paper we present a framework for the semi-automatic extraction of medical entities from referral letters and use them to transcribe a case report form. Our framework offers the functionality to: (a) extract the medical entity from the unstructured referral letters, (b) classify them according to their semantic type, and (c) transcribe a case report form based on the extracted information from the referral letter. We take a semantic text analytics approach where SNOMED-CT ontology is used to both classify referral concepts and to establish semantic similarities between referral concepts and CRF elements. We used 100 spine injury referral letters, and a standard case report form used by Association of Dalhousie Neurosurgeons, Dalhousie University},
keywords = {Referral letters, semantic mapping, Unstructured text analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we present a framework for the semi-automatic extraction of medical entities from referral letters and use them to transcribe a case report form. Our framework offers the functionality to: (a) extract the medical entity from the unstructured referral letters, (b) classify them according to their semantic type, and (c) transcribe a case report form based on the extracted information from the referral letter. We take a semantic text analytics approach where SNOMED-CT ontology is used to both classify referral concepts and to establish semantic similarities between referral concepts and CRF elements. We used 100 spine injury referral letters, and a standard case report form used by Association of Dalhousie Neurosurgeons, Dalhousie University