@inproceedings{dutra-etal-2026-frame,
title = "Frame Semantic Patterns for Identifying Underreporting of Notifiable Events in Healthcare: The Case of Gender-Based Violence",
author = "Dutra, L{\'i}via and
Lorenzi, Arthur and
Berno, Lais and
Campos, Franciany and
Biscardi, Karoline and
Brown, Kenneth and
Viridiano, Marcelo and
Belcavello, Frederico and
Matos, Ely E. and
Guaranha, Olivia and
Santos, Erik and
Reinach, Sofia and
Torrent, Tiago Timponi",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.51/",
doi = "10.63317/47vpwbo6vbxc",
pages = "694--704",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Frame Semantic Patterns for Identifying Underreporting of Notifiable Events in Healthcare: The Case of Gender-Based Violence
%A Dutra, Lívia
%A Lorenzi, Arthur
%A Berno, Lais
%A Campos, Franciany
%A Biscardi, Karoline
%A Brown, Kenneth
%A Viridiano, Marcelo
%A Belcavello, Frederico
%A Matos, Ely E.
%A Guaranha, Olivia
%A Santos, Erik
%A Reinach, Sofia
%A Torrent, Tiago Timponi
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F dutra-etal-2026-frame
%X 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.
%R 10.63317/47vpwbo6vbxc
%U https://aclanthology.org/2026.lrec-1.51/
%U https://doi.org/10.63317/47vpwbo6vbxc
%P 694-704
Markdown (Informal)
[Frame Semantic Patterns for Identifying Underreporting of Notifiable Events in Healthcare: The Case of Gender-Based Violence](https://aclanthology.org/2026.lrec-1.51/) (Dutra et al., LREC 2026)
ACL
- Lívia Dutra, Arthur Lorenzi, Lais Berno, Franciany Campos, Karoline Biscardi, Kenneth Brown, Marcelo Viridiano, Frederico Belcavello, Ely E. Matos, Olivia Guaranha, Erik Santos, Sofia Reinach, and Tiago Timponi Torrent. 2026. Frame Semantic Patterns for Identifying Underreporting of Notifiable Events in Healthcare: The Case of Gender-Based Violence. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 694–704, Palma de Mallorca, Spain. ELRA Language Resource Association.