Advancing Italian Biomedical Information Extraction with Transformers-based Models: Methodological Insights and Multicenter Practical Application
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866914640928178176 |
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| author | Crema, Claudio Buonocore, Tommaso Mario Fostinelli, Silvia Parimbelli, Enea Verde, Federico Fundarò, Cira Manera, Marina Ramusino, Matteo Cotta Capelli, Marco Costa, Alfredo Binetti, Giuliano Bellazzi, Riccardo Redolfi, Alberto |
| author_facet | Crema, Claudio Buonocore, Tommaso Mario Fostinelli, Silvia Parimbelli, Enea Verde, Federico Fundarò, Cira Manera, Marina Ramusino, Matteo Cotta Capelli, Marco Costa, Alfredo Binetti, Giuliano Bellazzi, Riccardo Redolfi, Alberto |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_05323 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Advancing Italian Biomedical Information Extraction with Transformers-based Models: Methodological Insights and Multicenter Practical Application Crema, Claudio Buonocore, Tommaso Mario Fostinelli, Silvia Parimbelli, Enea Verde, Federico Fundarò, Cira Manera, Marina Ramusino, Matteo Cotta Capelli, Marco Costa, Alfredo Binetti, Giuliano Bellazzi, Riccardo Redolfi, Alberto Computation and Language Artificial Intelligence Machine Learning I.2.7; J.3 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. |
| title | Advancing Italian Biomedical Information Extraction with Transformers-based Models: Methodological Insights and Multicenter Practical Application |
| topic | Computation and Language Artificial Intelligence Machine Learning I.2.7; J.3 |
| url | https://arxiv.org/abs/2306.05323 |