Title: Nature-inspired query optimisation models for medical information retrieval with relevance feedback

Authors: Aditya Jayasimha; Rahul Mudambi; S. Sowmya Kamath

Addresses: Automatic Speech Recognition, Samsung Research Institute Bangalore, Bangalore, 560003, Karnataka, India ' OYO Vacation Homes, OYO Hotels & Homes, Bangalore, 560010, Karnataka, India ' Department of Information Technology, National Institute of Technology Karnataka, Surathkal, 575025, Karnataka, India

Abstract: Medical information retrieval (MedIR) involves retrieving relevant medical-related information from a set of medical documents for a particular user query. As the volume of medical records grows, the challenging problem is determining those documents which best suiting a given query by considering user satisfaction. Statistical term weighting and probabilistic approaches for this purpose have several limitations. The gap between information need and user query can be addressed through query optimisation and relevance feedback. In this paper, we propose a document retrieval framework that incorporates query optimisation using techniques like genetic algorithm, particle swarm optimisation (PSO), and global swarm optimisation (GSO). Further, we use relevance feedback methods to reformulate the user query. The proposed techniques are applied to datasets with predefined relevance judgments to perform quantitative validation. Experimental results using the relevance judgements available in the University of Glasgow's Medline collection underscored the significant improvement achieved using BM25 scores as the fitness function.

Keywords: MedIR; medical information retrieval; relevance feedback; meta-heuristic algorithms; clinical IR; genetic algorithm; PSO; particle swarm optimisation; GSO; global swarm optimisation; cosine similarity score; Okapi BM25 score.

DOI: 10.1504/IJAIP.2023.133255

International Journal of Advanced Intelligence Paradigms, 2023 Vol.26 No.1, pp.43 - 59

Received: 02 May 2019
Accepted: 18 Nov 2019
Published online: 04 Sep 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article