Mammography reporting dataset with BI-RADS system for natural language processing applications: Addressing public data gaps in SpanishZENODO

Mammography reporting dataset with BI-RADS system for natural language processing applications: Addressing public data gaps in SpanishZENODO

Applying Natural Language Processing (NLP) to clinical reports is important for automating the analysis and classification of clinical data, improving diagnostic accuracy, and enhancing healthcare workflows. This article presents a dataset derived from mammography reports written in Spanish collecte...

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Bibliographic Details
Main Authors: José Luis Vázquez Noguera, Alejandro Torres-Hurtado, Helena Gómez-Adorno, Julio César Mello-Román, Enrique Javier Fleitas-Alvarez, Federico Fernando Espinola Schulze, Miguel Garcia-Torres, Carlos Domingo Méndez Gaona, Pedro Esteban Gardel Sotomayor, Silvia Vázquez Noguera, Norma Elizabeth Zaracho Amarilla, Oxades Wilfrido Gamarra Esquivel
Format: Article
Language:English
Published: Elsevier 2025-08-01
Series:Data in Brief
Subjects:
Mammography reports
Natural language processing
BI-RADS classification
Clinical data
Online Access:http://www.sciencedirect.com/science/article/pii/S2352340925004883
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Summary:Applying Natural Language Processing (NLP) to clinical reports is important for automating the analysis and classification of clinical data, improving diagnostic accuracy, and enhancing healthcare workflows. This article presents a dataset derived from mammography reports written in Spanish collected across multiple medical units operated by the Oxades company in Paraguay. The dataset contains 4357 records and 15 variables, including the text of the complete report and also each of its sections separately (clinical observations, diagnostic conclusions, follow-up recommendations), and the BI-RADS (Breast Imaging Reporting and Data System) classification assigned to each one of the reports. Additionally, the dataset includes metadata such as report IDs, dates, and patient information such as age, patient reasons for the analysis, last menstruation period, type of hormonal therapy received, family history and number of children. To ensure patient confidentiality, all identifiable data was removed, and the dataset was structured using automated segmentation and manual verification to ensure quality and transparency. This dataset is an invaluable resource for both medical and AI research communities. It provides real-world data for developing and testing NLP algorithms and machine learning models, specifically for automating BI-RADS classification and analyzing mammography reports.
ISSN:2352-3409

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