Automated Extraction and Maturity Analysis of Open Source Clinical Informatics Repositories from Scientific Literature

• URL: http://arxiv.org/abs/2403.14721v2
• Date: Wed, 27 Mar 2024 17:36:09 GMT
• Title: Automated Extraction and Maturity Analysis of Open Source Clinical Informatics Repositories from Scientific Literature
• Authors: Jeremy R. Harper,
• Abstract summary: This study introduces an automated methodology to bridge the gap by systematically extracting GitHub repository URLs from academic papers indexed in arXiv. Our approach encompasses querying the arXiv API for relevant papers, cleaning extracted GitHub URLs, fetching comprehensive repository information via the GitHub API, and analyzing repository maturity based on defined metrics such as stars, forks, open issues, and contributors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In the evolving landscape of clinical informatics, the integration and utilization of software tools developed through governmental funding represent a pivotal advancement in research and application. However, the dispersion of these tools across various repositories, with no centralized knowledge base, poses significant challenges to leveraging their full potential. This study introduces an automated methodology to bridge this gap by systematically extracting GitHub repository URLs from academic papers indexed in arXiv, focusing on the field of clinical informatics. Our approach encompasses querying the arXiv API for relevant papers, cleaning extracted GitHub URLs, fetching comprehensive repository information via the GitHub API, and analyzing repository maturity based on defined metrics such as stars, forks, open issues, and contributors. The process is designed to be robust, incorporating error handling and rate limiting to ensure compliance with API constraints. Preliminary findings demonstrate the efficacy of this methodology in compiling a centralized knowledge base of NIH-funded software tools, laying the groundwork for an enriched understanding and utilization of these resources within the clinical informatics community. We propose the future integration of Large Language Models (LLMs) to generate concise summaries and evaluations of the tools. This approach facilitates the discovery and assessment of clinical informatics tools and also enables ongoing monitoring of new and actively updated repositories, revolutionizing how researchers access and leverage federally funded software. The implications of this study extend beyond simplification of access to valuable resources; it proposes a scalable model for the dynamic aggregation and evaluation of scientific software, encouraging more collaborative, transparent, and efficient research practices in clinical informatics and beyond.

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