Automatic de-identification of French electronic health records: a cost-effective approach exploiting distant supervision and deep learning models

Autor: Mohamed El Azzouzi, Gouenou Coatrieux, Reda Bellafqira, Denis Delamarre, Christine Riou, Naima Oubenali, Sandie Cabon, Marc Cuggia, Guillaume Bouzillé

Publikováno v: BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-18 (2024)

Abstract Background Electronic health records (EHRs) contain valuable information for clinical research; however, the sensitive nature of healthcare data presents security and confidentiality challenges. De-identification is therefore essential to pr

Externí odkaz: https://doaj.org/article/e93c4f82eaa84b88bf5ffd1185ee8736

A Novel COVID-19 Data Set and an Effective Deep Learning Approach for the De-Identification of Italian Medical Records

Autor: Rosario Catelli, Francesco Gargiulo, Valentina Casola, Giuseppe De Pietro, Hamido Fujita, Massimo Esposito

Publikováno v: IEEE Access, Vol 9, Pp 19097-19110 (2021)

In the last years, the need to de-identify privacy-sensitive information within Electronic Health Records (EHRs) has become increasingly felt and extremely relevant to encourage the sharing and publication of their content in accordance with the rest

Externí odkaz: https://doaj.org/article/ea5f09c704cd49d18ed17ce5d46d11b4

Combining contextualized word representation and sub-document level analysis through Bi-LSTM+CRF architecture for clinical de-identification

Autor: Rosario Catelli, Valentina Casola, Massimo Esposito, Giuseppe De Pietro, Hamido Fujita

Publikováno v: Knowledge-based systems 213 (2021). doi:10.1016/j.knosys.2020.106649
info:cnr-pdr/source/autori:Catelli R.; Casola V.; De Pietro G.; Fujita H.; Esposito M./titolo:Combining contextualized word representation and sub-document level analysis through Bi-LSTM+CRF architecture for clinical de-identification/doi:10.1016%2Fj.knosys.2020.106649/rivista:Knowledge-based systems/anno:2021/pagina_da:/pagina_a:/intervallo_pagine:/volume:213

Clinical de-identification aims to identify Protected Health Information in clinical data, enabling data sharing and publication. First automatic de-identification systems were based on rules or on machine learning methods, limited by language change

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e1cfdf98435ce7c3994d43ddd2c4ba39
http://hdl.handle.net/11588/837696

Crosslingual named entity recognition for clinical de-identification applied to a COVID-19 Italian data set

Autor: Massimo Esposito, Francesco Gargiulo, Valentina Casola, Hamido Fujita, Giuseppe De Pietro, Rosario Catelli

Publikováno v: Applied Soft Computing

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 P

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f54b85f13057cd04653731500df0748

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