ArticleApplied soft computing2020
Crosslingual named entity recognition for clinical de-identification applied to a COVID-19 Italian data set.
Article in Applied soft computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- DA-BioNER: data augmentation based on few-shot learning and distant supervision for biomedical named entity recognition.Bioinformatics (Oxford, England) · 2026Article
- Bangla MedER: Multi-BERT ensemble approach for the recognition of Bangla medical entity.PloS one · 2026Article
- Cross- & multi-lingual medication detection: a transformer-based analysis.BMC medical informatics and decision making · 2025Article
- Article
- GRU-SCANET: unleashing the power of GRU-based sinusoidal capture network for precision-driven named entity recognition.Bioinformatics advances · 2025Article
- Not Fully Synthetic: LLM-based Hybrid Approaches Towards Privacy-Preserving Clinical Note Sharing.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
- Web-Based Application Based on Human-in-the-Loop Deep Learning for Deidentifying Free-Text Data in Electronic Medical Records: Development and Usability Study.Interactive journal of medical research · 2023Article
- Impact of word embedding models on text analytics in deep learning environment: a review.Artificial intelligence review · 2023Article
- A Review of the Machine Learning Algorithms for Covid-19 Case Analysis.IEEE transactions on artificial intelligence · 2023Article
- De-identification of free text data containing personal health information: a scoping review of reviews.International journal of population data science · 2023Article
- De-identifying Norwegian Clinical Text using Resources from Swedish and Danish.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes.Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) · 2022Article
- Cure models to estimate time until hospitalization due to COVID-19: A case study in Galicia (NW Spain).Applied intelligence (Dordrecht, Netherlands) · 2022Article
- Statistical analysis of the community lockdown for COVID-19 pandemic.Applied intelligence (Dordrecht, Netherlands) · 2022Article
- Data driven covid-19 spread prediction based on mobility and mask mandate information.Applied intelligence (Dordrecht, Netherlands) · 2022Article
- Intelligent system for COVID-19 prognosis: a state-of-the-art survey.Applied intelligence (Dordrecht, Netherlands) · 2021Article
- Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component.Applied intelligence (Dordrecht, Netherlands) · 2021Article
- The analysis of isolation measures for epidemic control of COVID-19.Applied intelligence (Dordrecht, Netherlands) · 2021Article
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6 authors.
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Abstract
The COrona VIrus Disease 19 (COVID-19) pandemic required the work of all global experts to tackle it. Despite the abundance of new studies, privacy laws prevent their dissemination for medical investigations: through clinical de-identification, the Protected Health Information (PHI) contained therein can be anonymized so that medical records can be shared and published. The automation of clinical de-identification through deep learning techniques has proven to be less effective for languages other than English due to the scarcity of data sets. Hence a new Italian de-identification data set has been created from the COVID-19 clinical records made available by the Italian Society of Radiology (SIRM). Therefore, two multi-lingual deep learning systems have been developed for this low-resource language scenario: the objective is to investigate their ability to transfer knowledge between different languages while maintaining the necessary features to correctly perform the Named Entity Recognition task for de-identification. The systems were trained using four different strategies, using both the English Informatics for Integrating Biology & the Bedside (i2b2) 2014 and the new Italian SIRM COVID-19 data sets, then evaluated on the latter. These approaches have demonstrated the effectiveness of cross-lingual transfer learning to de-identify medical records written in a low resource language such as Italian, using one with high resources such as English.
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