Výsledky vyhledávání - "clinical de-identification"

Report

Transferability of Neural Network Clinical De-identification Systems

Autor: Lee, Kahyun, Dobbins, Nicholas J., McInnes, Bridget, Yetisgen, Meliha, Uzuner, Ozlem

Objective: Neural network de-identification studies have focused on individual datasets. These studies assume the availability of a sufficient amount of human-annotated data to train models that can generalize to corresponding test data. In real-worl

Externí odkaz: http://arxiv.org/abs/2102.08517

Zobrazit plný text záznamu

Akademický článek

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

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

Publikováno v: In Knowledge-Based Systems 15 February 2021 213

Zobrazit plný text záznamu

Akademický článek

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

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

Publikováno v: In Applied Soft Computing Journal December 2020 97 Part A

Zobrazit plný text záznamu

Akademický článek

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

Akademický článek

Investigation of the Utility of Features in a Clinical De-identification Model: A Demonstration Using EHR Pathology Reports for Advanced NSCLC Patients

Autor: Tanmoy Paul, Md Kamruz Zaman Rana, Preethi Aishwarya Tautam, Teja Venkat Pavan Kotapati, Yaswitha Jampani, Nitesh Singh, Humayera Islam, Vasanthi Mandhadi, Vishakha Sharma, Michael Barnes, Richard D. Hammer, Abu Saleh Mohammad Mosa

Publikováno v: Frontiers in Digital Health, Vol 4 (2022)

BackgroundElectronic health record (EHR) systems contain a large volume of texts, including visit notes, discharge summaries, and various reports. To protect the confidentiality of patients, these records often need to be fully de-identified before c

Externí odkaz: https://doaj.org/article/3828d9965431427887fb90453f48bc56

Zobrazit plný text záznamu

Akademický článek

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

Akademický článek

Utility of Features in a Natural-Language-Processing-Based Clinical De-Identification Model Using Radiology Reports for Advanced NSCLC Patients

Autor: Tanmoy Paul, Humayera Islam, Nitesh Singh, Yaswitha Jampani, Teja Venkat Pavan Kotapati, Preethi Aishwarya Tautam, Md Kamruz Zaman Rana, Vasanthi Mandhadi, Vishakha Sharma, Michael Barnes, Richard D. Hammer, Abu Saleh Mohammad Mosa

Publikováno v: Applied Sciences, Vol 12, Iss 19, p 9976 (2022)

The de-identification of clinical reports is essential to protect the confidentiality of patients. The natural-language-processing-based named entity recognition (NER) model is a widely used technique of automatic clinical de-identification. The perf

Externí odkaz: https://doaj.org/article/e0a2cb981dc5479694e7a9dccdcb2ccd

Zobrazit plný text záznamu

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

Zobrazit plný text záznamu

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

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání