Advancing Italian Biomedical Information Extraction with Transformers-based Models: Methodological Insights and Multicenter Practical Application
This work addresses the challenge of extracting data from unstructured Italian medical records for clinical practitioners, representing an incremental advancement by applying existing methods to a new, less-resourced language domain.
The authors tackled the problem of underutilized unstructured medical records in Italian neuropsychiatry by creating the first Italian neuropsychiatric Named Entity Recognition dataset, PsyNIT, and developing a Transformers-based model with an overall F1-score of 84.77%, Precision of 83.16%, and Recall of 86.44%.
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.