Authors:
Faiza Hussain
and
Usman Qamar
Affiliation:
National University of Sciences and Technology (NUST), Pakistan
Keyword(s):
Spelling Correction, Electronic Medical Record, EMR, Natural Language Processing, Text Mining, Information Retrieval, POST, Parts-of-Speech Tagging, Regular Expressions and Medical Text Processing.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Cloud Computing
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Semantic Web Technologies
;
Sensor Networks
;
Services Science
;
Signal Processing
;
Soft Computing
;
Software Agents and Internet Computing
;
Software Engineering
Abstract:
Medications are an important element of medical records but they usually contain significant data errors. This situation may result from haphazardness or possibly careless storage of valuable information. In either case, this misspelled data can cause serious health problems for the patients and can put their life at a major risk. Thus, the correctness of medication data is an important aspect so that potential harms can be identified and steps can be taken to prevent or mitigate them. In this paper, a novel and practical method is proposed for automated detection and correction of spelling errors in electronic medical record (EMR). To realize this technique, major relevant aspects is taken into consideration with the help of Parts-of-Speech tagging and Regular Expressions. The paper concludes with recommendations and future work for giving a new direction to the emendation of drug nomenclature.