Frame Semantic Patterns for Identifying Underreporting of Notifiable Events in Healthcare: The Case of Gender-Based Violence
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arXiv
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| Format: | Preprint |
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2025
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| author | Dutra, Lívia Lorenzi, Arthur Berno, Laís Campos, Franciany Biscardi, Karoline Brown, Kenneth Viridiano, Marcelo Belcavello, Frederico Matos, Ely Guaranha, Olívia Santos, Erik Reinach, Sofia Torrent, Tiago Timponi |
| author_facet | Dutra, Lívia Lorenzi, Arthur Berno, Laís Campos, Franciany Biscardi, Karoline Brown, Kenneth Viridiano, Marcelo Belcavello, Frederico Matos, Ely Guaranha, Olívia Santos, Erik Reinach, Sofia Torrent, Tiago Timponi |
| contents | We introduce a methodology for the identification of notifiable events in the domain of healthcare. The methodology harnesses semantic frames to define fine-grained patterns and search them in unstructured data, namely, open-text fields in e-medical records. We apply the methodology to the problem of underreporting of gender-based violence (GBV) in e-medical records produced during patients' visits to primary care units. A total of eight patterns are defined and searched on a corpus of 21 million sentences in Brazilian Portuguese extracted from e-SUS APS. The results are manually evaluated by linguists and the precision of each pattern measured. Our findings reveal that the methodology effectively identifies reports of violence with a precision of 0.726, confirming its robustness. Designed as a transparent, efficient, low-carbon, and language-agnostic pipeline, the approach can be easily adapted to other health surveillance contexts, contributing to the broader, ethical, and explainable use of NLP in public health systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26969 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Frame Semantic Patterns for Identifying Underreporting of Notifiable Events in Healthcare: The Case of Gender-Based Violence Dutra, Lívia Lorenzi, Arthur Berno, Laís Campos, Franciany Biscardi, Karoline Brown, Kenneth Viridiano, Marcelo Belcavello, Frederico Matos, Ely Guaranha, Olívia Santos, Erik Reinach, Sofia Torrent, Tiago Timponi Computation and Language Artificial Intelligence We introduce a methodology for the identification of notifiable events in the domain of healthcare. The methodology harnesses semantic frames to define fine-grained patterns and search them in unstructured data, namely, open-text fields in e-medical records. We apply the methodology to the problem of underreporting of gender-based violence (GBV) in e-medical records produced during patients' visits to primary care units. A total of eight patterns are defined and searched on a corpus of 21 million sentences in Brazilian Portuguese extracted from e-SUS APS. The results are manually evaluated by linguists and the precision of each pattern measured. Our findings reveal that the methodology effectively identifies reports of violence with a precision of 0.726, confirming its robustness. Designed as a transparent, efficient, low-carbon, and language-agnostic pipeline, the approach can be easily adapted to other health surveillance contexts, contributing to the broader, ethical, and explainable use of NLP in public health systems. |
| title | Frame Semantic Patterns for Identifying Underreporting of Notifiable Events in Healthcare: The Case of Gender-Based Violence |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2510.26969 |