Frame Semantic Patterns for Identifying Underreporting of Notifiable Events in Healthcare: The Case of Gender-Based Violence

TL;DR

This paper presents a semantic pattern-based NLP methodology to detect underreported gender-based violence in healthcare records, demonstrating high precision and adaptability for health surveillance.

Contribution

The paper introduces a novel, language-agnostic semantic pattern approach for identifying underreported health events in unstructured medical data.

Findings

Precision of 0.726 in identifying violence reports

Effective detection of underreported gender-based violence

Method is transparent, efficient, and adaptable to other contexts

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.