{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T06:35:55Z","timestamp":1762929355993,"version":"3.45.0"},"reference-count":19,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Patient-generated drug reviews are becoming increasingly available and serve as a rich source for computational drug prioritization.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>\n                      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\n                      <jats:ext-link>https:\/\/github.com\/suruthi-m\/Hybrid_PSO_EAVOA<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdgth.2025.1708730","type":"journal-article","created":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T06:31:34Z","timestamp":1762929094000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A hybrid PSO-AVOA framework for patient-reported drug prioritization with enhanced exploration and 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