A hybrid PSO-AVOA framework for patient-reported drug prioritization with enhanced exploration and exploitation

TL;DR

This paper introduces a new optimization algorithm combining PSO and EAVOA to prioritize drugs based on patient reviews, improving convergence and solution quality.

Contribution

A novel hybrid PSO-EAVOA algorithm with enhanced exploration and exploitation for drug prioritization using patient-reported data.

Findings

Hybrid PSO-EAVOA outperformed five state-of-the-art algorithms in convergence speed and solution quality.

The framework effectively aggregates drug review data on effectiveness, side-effects, and consistency.

The method is compatible with various aggregation techniques and applicable to pharmacovigilance and drug re-purposing.

Abstract

Patient-generated drug reviews are becoming increasingly available and serve as a rich source for computational drug prioritization.

In this study, we developed a Hybrid Particle Swarm-Enhanced African Vulture Optimisation Algorithm (Hybrid PSO-EAVOA) that fosters the development of better balances between the exploration and exploitation of which the framework uses the improved opposition-based learning, Levy flights, and elite preservation approaches. In the framework, multiple evaluation criteria are accommodated, recovering value in the form of an overall single-objective optimization scheme, where effectiveness, side-effects, and consistency of reviews were compiled for clinical significance and combined by a weighted-sum fitness function. To validate the experiment using a large-scale dataset of drug reviews obtained from the Drugs Side Effects and Medical Condition dataset sourced from Drugs.com in Kaggle.

Hybrid PSO-EAVOA performed a benchmark comparison against five state-of-the-art metaheuristic algorithms (PSO, EAVOA, WHO, ALO, and HOA) using varying iterations as runs. In each comparison, Hybrid PSO-EAVOA achieved superior or better convergence speed, robustness, and quality of solutions.

The specific method of weighted-sum aggregation was used in this study, the framework offered could be easily compatible with other forms of aggregation. Hybrid PSO-EAVOA demonstrates strong potential for broader application in fields such as pharmacovigilance, clinical decision support, and drug re-purposing. The dataset is publicly available on Kaggle Drugs Side Effects and Medical Condition and all source code for parameter settings and preprocessing scripts is publicly available at the GitHub repository https://github.com/suruthi-m/Hybrid_PSO_EAVOA.

Figures