Advancing Italian Biomedical Information Extraction with Transformers-based Models: Methodological Insights and Multicenter Practical Application

• URL: http://arxiv.org/abs/2306.05323v2
• Date: Mon, 15 Jan 2024 11:05:23 GMT
• Title: Advancing Italian Biomedical Information Extraction with Transformers-based Models: Methodological Insights and Multicenter Practical Application
• Authors: Claudio Crema, Tommaso Mario Buonocore, Silvia Fostinelli, Enea Parimbelli, Federico Verde, Cira Fundar\`o, Marina Manera, Matteo Cotta Ramusino, Marco Capelli, Alfredo Costa, Giuliano Binetti, Riccardo Bellazzi and Alberto Redolfi
• Abstract summary: Information Extraction can help clinical practitioners overcome the limitation by using automated text-mining pipelines. We created the first Italian neuropsychiatric Named Entity Recognition dataset, PsyNIT, and used it to develop a Transformers-based model. The lessons learned are: (i) the crucial role of a consistent annotation process and (ii) a fine-tuning strategy that combines classical methods with a "low-resource" approach.
• Score: 0.27027468002793437
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The introduction of computerized medical records in hospitals has reduced burdensome activities like manual writing and information fetching. However, the data contained in medical records are still far underutilized, primarily because extracting data from unstructured textual medical records takes time and effort. Information Extraction, a subfield of Natural Language Processing, can help clinical practitioners overcome this limitation by using automated text-mining pipelines. In this work, we created the first Italian neuropsychiatric Named Entity Recognition dataset, PsyNIT, and used it to develop a Transformers-based model. Moreover, we collected and leveraged three external independent datasets to implement an effective multicenter model, with overall F1-score 84.77%, Precision 83.16%, Recall 86.44%. The lessons learned are: (i) the crucial role of a consistent annotation process and (ii) a fine-tuning strategy that combines classical methods with a "low-resource" approach. This allowed us to establish methodological guidelines that pave the way for Natural Language Processing studies in less-resourced languages.

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