Automatic detection of protected health information from clinic narratives.
about
Entity recognition from clinical texts via recurrent neural network.Practical applications for natural language processing in clinical research: The 2014 i2b2/UTHealth shared tasks.Learning to identify Protected Health Information by integrating knowledge- and data-driven algorithms: A case study on psychiatric evaluation notes.Automatic prediction of coronary artery disease from clinical narratives.A hybrid approach to automatic de-identification of psychiatric notes.De-identification of clinical notes via recurrent neural network and conditional random field.The UAB Informatics Institute and 2016 CEGS N-GRID de-identification shared task challenge.Scalable Iterative Classification for Sanitizing Large-Scale Datasets.De-identification of patient notes with recurrent neural networks.De-identification of medical records using conditional random fields and long short-term memory networks.Leveraging text skeleton for de-identification of electronic medical records.Leveraging existing corpora for de-identification of psychiatric notes using domain adaptation.HEDEA: A Python Tool for Extracting and Analysing Semi-structured Information from Medical Records.
P2860
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P2860
Automatic detection of protected health information from clinic narratives.
description
2015 nî lūn-bûn
@nan
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
2015年论文
@zh
2015年论文
@zh-cn
name
Automatic detection of protected health information from clinic narratives.
@en
type
label
Automatic detection of protected health information from clinic narratives.
@en
prefLabel
Automatic detection of protected health information from clinic narratives.
@en
P2860
P1476
Automatic detection of protected health information from clinic narratives.
@en
P2093
Jonathan M Garibaldi
P2860
P356
10.1016/J.JBI.2015.06.015
P478
P577
2015-07-28T00:00:00Z