Kranthi Kiran Jalakam

@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}

}