The current biomedical literature is huge, unstructured, and complex, posing a significant challenge to efficient information processing and consequently creating a substantial gap between medical research and clinical practice. This underscores the need for innovative approaches to convert unstructured information into a computable format readily available for clinical decision-making. The study aims to leverage large language models (LLMs) to accurately extract health evidence from unstructured biomedical text by utilizing the PICO (Patient, Intervention, Comparison, Outcome) framework. We implemented different variations of Generative Pre-trained Transformers (GPT), where GPT 4 and GPT 4 Turbo Preview consistently improve recall, precision, and accuracy compared to competitors like GPT 4o and GPT 3.5. We employed the document retrieval strategy, which involved the use of the "stuff method" instead of the "map-reduce" and "map-rerank" methods, aligning to achieve higher accuracy within a limited document set. Challenges include accurately synthesizing fragmented PICO elements and ensuring answer correctness due to limitations in context retrieval length. The performance of the proposed system is evaluated using the Retrieval Augmented Generation assessment (RAGAs) framework. The findings highlight LLMs’ potential in medical research while emphasizing the need for enhancements in context retrieval and model correctness. The study’s outcomes suggest that leveraging LLMs for biomedical information extraction can potentially improve the healthcare decision-making landscape.

This paper presents a novel technique for enhancing the allocation of resources in the charging infrastructure for e-bikes by employing deep reinforcement learning (DRL) in a context-specific manner. With the evolving transportation land-scape and the increasing popularity of electric micro-mobility solutions, efficient resource management plays a vital role. The methodology involves utilizing deep Q networks (DQN) in virtual environments to dynamically allocate charging resources based on contextual parameters. Through empirical analysis, we demonstrate the efficacy of our approach in achieving optimal resource allocation while considering the needs of diverse stakeholders. Findings suggest that the DQN model, when integrated with context-awareness and stakeholder objectives, enables efficient resource allocation, maximize fleet utilization, ensures charging station availability, enhances user satisfaction, and promotes environmental sustainability. The findings of this study offer valuable insights for policymakers, urban planners, and industry stakeholders seeking to enhance the sustainability and effectiveness of micro-mobility services in urban settings. Overall, this study contributes to the advancement of sustainable urban mobility solutions and underscores the potential of DRL in addressing complex urban transportation challenges.