CRFs based de-identification of medical records

He, Bin; Guan, Yi; Cheng, Jianyi; Cen, Keting; Hua, Wenlan

doi:10.1016/j.jbi.2015.08.012

Cited by 40 publications

(35 citation statements)

References 13 publications

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“…Over the years, the effective but highly complex rulesbased methods [20] have given way to machine learning systems where large amounts of data with easy-to-extract features were available for the training phase, [21]- [23] or to hybrid systems capable of detecting entities even in cases of scarcity of data and complex features, provided that they are more sophisticated and take more time to be implemented [24]- [27]. These machine learning (ML) algorithms have modeled the NER problem, i.e.…”

Section: A Rules and Machine Learning Approachesmentioning

confidence: 99%

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

et al. 2021

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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 restrictions imposed by both national and supranational privacy authorities. In the field of Natural Language Processing (NLP), several deep learning techniques for Named Entity Recognition (NER) have been applied to face this issue, significantly improving the effectiveness in identifying sensitive information in EHRs written in English. However, the lack of data sets in other languages has strongly limited their applicability and performance evaluation. To this aim, a new de-identification data set in Italian has been developed in this work, starting from the 115 COVID-19 EHRs provided by the Italian Society of Radiology (SIRM): 65 were used for training and development, the remaining 50 were used for testing. The data set was labelled following the guidelines of the i2b2 2014 de-identification track. As additional contribution, combined with the best performing Bi-LSTM + CRF sequence labeling architecture, a stacked word representation form, not yet experimented for the Italian clinical de-identification scenario, has been tested, based both on a contextualized linguistic model to manage word polysemy and its morpho-syntactic variations and on sub-word embeddings to better capture latent syntactic and semantic similarities. Finally, other cutting-edge approaches were compared with the proposed model, which achieved the best performance highlighting the goodness of the promoted approach. INDEX TERMS clinical de-identification, contextualized embedding, deep learning, Italian language, named entity recognition

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Section: A Rules and Machine Learning Approachesmentioning

confidence: 99%

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

et al. 2021

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“…Lots of teams from all around the world participated in this three challenges. In the two i2b2 NLP challenges, the proposed de-identification systems may fall in three categories: rule-based [26], machine learning-based [10, 14, 27], and hybrid [11, 12, 13, 15]. The rule-based systems can exactly recognize formulaic PHI instances (i.e., phone numbers, emails, licenses, etc.…”

Section: Introductionmentioning

confidence: 99%

De-identification of clinical notes via recurrent neural network and conditional random field

Liu

Tang

Wang

et al. 2017

Journal of Biomedical Informatics

121

114

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De-identification, identifying information from data, such as protected health information (PHI) present in clinical data, is a critical step to enable data to be shared or published. The 2016 Centers of Excellence in Genomic Science (CEGS) Neuropsychiatric Genome-scale and RDOC Individualized Domains (N-GRID) clinical natural language processing (NLP) challenge contains a de-identification track in de-identifying electronic medical records (EMRs) (i.e., track 1). The challenge organizers provide 1000 annotated mental health records for this track, 600 out of which are used as a training set and 400 as a test set. We develop a hybrid system for the de-identification task on the training set. Firstly, four individual subsystems, that is, a subsystem based on bidirectional LSTM (long-short term memory, a variant of recurrent neural network), a subsystem-based on bidirectional LSTM with features, a subsystem based on conditional random field (CRF) and a rule-based subsystem, are used to identify PHI instances. Then, an ensemble learning-based classifiers is deployed to combine all PHI instances predicted by above three machine learning-based subsystems. Finally, the results of the ensemble learning-based classifier and the rule-based subsystem are merged together. Experiments conducted on the official test set show that our system achieves the highest micro F1-scores of 93.07%, 91.43% and 95.23% under the “token”, “strict” and “binary token” criteria respectively, ranking first in the 2016 CEGS N-GRID NLP challenge. In addition, on the dataset of 2014 i2b2 NLP challenge, our system achieves the highest micro F1-scores of 96.98%, 95.11% and 98.28% under the “token”, “strict” and “binary token” criteria respectively, outperforming other state-of-the-art systems. All these experiments prove the effectiveness of our proposed method.

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“…One evidence is that the performance of the participating systems in the 2016 track was poorer than that in the 2014 track(maximum = 0.936, median = 0.845). Another is that we re-trained our participating system in 2014 track ( F 1 = 0.924) [32] directly on the 2016 training set, and achieved a much lower F 1 score of 0.823 on the test set.…”

Section: Discussionmentioning

confidence: 99%

De-identification of medical records using conditional random fields and long short-term memory networks

Jiang

Zhao

et al. 2017

Journal of Biomedical Informatics

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The CEGS N-GRID 2016 Shared Task 1 in Clinical Natural Language Processing focuses on the de-identification of psychiatric evaluation records. This paper describes two participating systems of our team, based on conditional random fields (CRFs) and long short-term memory networks (LSTMs). A pre-processing module was introduced for sentence detection and tokenization before de-identification. For CRFs, manually extracted rich features were utilized to train the model. For LSTMs, a character-level bi-directional LSTM network was applied to represent tokens and classify tags for each token, following which a decoding layer was stacked to decode the most probable protected health information (PHI) terms. The LSTM-based system attained an i2b2 strict micro-F1 measure of 89.86%, which was higher than that of the CRF-based system.

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CRFs based de-identification of medical records

Cited by 40 publications

References 13 publications

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

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

De-identification of clinical notes via recurrent neural network and conditional random field

De-identification of medical records using conditional random fields and long short-term memory networks

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