An enhanced concept based approach medical information retrieval to address readability, vocabulary and presentation issues

Querying of health information retrieval for health advice has now become a general and notable task performed by individuals on the Internet. However, the failure of the existing approaches to integrate program modules that would address the information needs of all categories of end-users remains. This study focused on proposing an improved framework and designing an enhanced concept based approach (ECBA) for medical information retrieval that would better address readability, vocabulary mismatched and presentation issues by generating medical discharge documents and medical search queries results in both medical expert and layman’s forms. Three special program modules were designed and integrated in the enhanced concept based approach namely: medical terms control module, vocabulary controlled module and readability module to specifically address the information needs of both medical experts and laymen end-users. Eight benched marked datasets namely: Medline, UMLS, MeSH, Metamap, Metathesaurus, Diagnosia 7, Khresmoi Project 6 and Genetic Home Reference were used in validating the systems performance. Additionally, the ECBA was compared using three existing approaches such as concept based approach (CBA), query likelihood model (QLM) and latent semantic indexing (LSI). The evaluation was conducted using the performance and statistical metrics: P@40, NDCG@40, MAP, Analysis of Variance (ANOVA) and Turkey HSD Tests. The outcome of the final experimental results obtained shows that, the ECBA consistently obtained above 93% accuracy rate results on Medline, UMLS and MeSH Datasets, 92% on Metamap, Metathesaurus and Diagnosia 7 datasets and 91% on Khresmoi Project 6 and Genetic Home Reference datasets. Also, the statistical analysis performance results obtained by each of the four approaches: ECBA, CBA, QLM and LSI shows that, there is a significant difference among their Mean Scores, hence, the null hypothesis of no significant difference was rejected.

Name dc:type.qualificationname

phd

Level dc:type.qualificationlevel

doctoral

Grantor dc:publisher.institution

Universiti Tun Hussein Onn Malaysia

Year dc:date.issued

2019

Author dc:creator

• Kontagora, Ibrahim Umar

• QA76 Computer software

Language dc:language

en