Enhancing the decision optimization of interaction design in sustainable healthcare with improved artificial bee colony algorithm and generative artificial intelligence

• Published in: PloS one Vol. 20; no. 2; p. e0317488
• Main Authors: Yu, Shuhui, Guan, Xin, Peng, Xiaoyan, Zeng, Yanzhao, Wang, Zeyu, Liang, Xinyi, Qin, Tianqiao, Zhou, Xiang
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 25.02.2025; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Artificial Intelligence; Bees; Biology and Life Sciences; Computational linguistics; Computer and Information Sciences; Convergence; Decision Making; Delivery of Health Care; Design optimization; E-health; Ecology and Environmental Sciences; Effectiveness; Efficiency; Energy consumption; Evolutionary algorithms; Exploration; Forecasts and trends; Generative Artificial Intelligence; Health aspects; Health care; Health care industry; Health care policy; Humans; Language processing; Lasers; Management; Medical personnel; Medical research; Medical technology; Medicine and Health Sciences; Natural language interfaces; Natural language processing; Optimization; Pareto optimum; Patients; Pharmaceuticals; Physical Sciences; Population; Quantum computing; R&D; Research & development; Research and Analysis Methods; Search algorithms; Social Sciences; Sustainable development; Swarm intelligence; Technology application; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0317488

With the development of digital health, enhancing decision-making effectiveness has become a critical task. This study proposes an improved Artificial Bee Colony (ABC) algorithm aimed at optimizing decision-making models in the field of digital health. The algorithm draws inspiration from the dual-layer evolutionary space of cultural algorithms, combining normative knowledge from the credibility space to dynamically adjust the search range, thereby improving both convergence speed and exploration capabilities. Additionally, a population dispersion strategy is introduced to maintain diversity, effectively balancing global exploration with local exploitation. Experimental results show that the improved ABC algorithm exhibits a 96% convergence probability when approaching the global optimal solution, significantly enhancing the efficiency and accuracy of medical resource optimization, particularly in complex decision-making environments. Integrating this algorithm with the Chat Generative Pre-trained Transformer (ChatGPT) decision system can intelligently generate personalized decision recommendations and leverage natural language processing technologies to better understand and respond to user needs. This study provides an effective tool for scientific decision-making in digital healthcare and offers critical technical support for processing and analyzing large-scale medical data.