Rapid Biomedical Research Classification: The Pandemic PACT Advanced Categorisation Engine
<|reference_start|>Rapid Biomedical Research Classification: The Pandemic PACT Advanced Categorisation Engine: This paper introduces the Pandemic PACT Advanced Categorisation Engine (PPACE) along with its associated dataset. PPACE is a fine-tuned model developed to automatically classify research abstracts from funded biomedical projects according to WHO-aligned research priorities. This task is crucial for monitoring research trends and identifying gaps in global health preparedness and response. Our approach builds on human-annotated projects, which are allocated one or more categories from a predefined list. A large language model is then used to generate `rationales' explaining the reasoning behind these annotations. This augmented data, comprising expert annotations and rationales, is subsequently used to fine-tune a smaller, more efficient model. Developed as part of the Pandemic PACT project, which aims to track and analyse research funding and clinical evidence for a wide range of diseases with outbreak potential, PPACE supports informed decision-making by research funders, policymakers, and independent researchers. We introduce and release both the trained model and the instruction-based dataset used for its training. Our evaluation shows that PPACE significantly outperforms its baselines. The release of PPACE and its associated dataset offers valuable resources for researchers in multilabel biomedical document classification and supports advancements in aligning biomedical research with key global health priorities.<|reference_end|>
@article{rohanian2024rapid,
    title={Rapid Biomedical Research Classification: The Pandemic PACT Advanced
  Categorisation Engine},
    author={Omid Rohanian, Mohammadmahdi Nouriborji, Olena Seminog, Rodrigo Furst,
  Thomas Mendy, Shanthi Levanita, Zaharat Kadri-Alabi, Nusrat Jabin, Daniela
  Toale, Georgina Humphreys, Emilia Antonio, Adrian Bucher, Alice Norton, David
  A. Clifton},
    journal={arXiv preprint arXiv:2407.10086},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10086},
    primaryClass={cs.CL cs.AI}
}