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

@article{Jiang2017DeidentificationOM,
  title={De-identification of medical records using conditional random fields and long short-term memory networks},
  author={Zhipeng Jiang and Chao Zhao and Bin He and Yi Guan and Jingchi Jiang},
  journal={Journal of biomedical informatics},
  year={2017},
  volume={75S},
  pages={
          S43-S53
        }
}
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22 Citations
Highly Influential Citations
3
Background Citations
6
Methods Citations
9

22 Citations

Citation Type

Citation Type

Survey on RNN and CRF models for de-identification of medical free text
TLDR
A comprehensive survey of work on automated free text de-identification with recurrent neural network (RNN) and conditional random field (CRF) approaches finds that RNN models, particularly long short-term memory (LSTM) algorithms, generally outperformed CRF models and also other systems, namely rule-based algorithms.
Redaction of Protected Health Information in EHRs using CRFs and Bi-directional LSTMs
TLDR
This paper proposes an efficient solution for redaction using two models, both of which achieve good F-scores for PHIs of all types and achieves a micro-F1 measure of 0.9592, which performs better than the CRF-based model.
A Short Survey of LSTM Models for De-identification of Medical Free Text
TLDR
Performance-wise, LSTMs generally surpassed other types of models used in automated de-identification of free text, namely conditional random field (CRF) algorithms and rule-based algorithms, but hybrid or ensemble LSTM models did not outperform L STM -only models.
Window Classifiers and Conditional Random Fields for Medical Report De-Identification
TLDR
Two methods, a window classifier and an implementation of conditional random fields (CRF) are presented in order to de-identify personal information of Spanish medical records provided by the MEDDOCAN challenge.
Publicly available machine learning models for identifying opioid misuse from the clinical notes of hospitalized patients
TLDR
Good test characteristics for an opioid misuse computable phenotype that is void of any PHI and performs similarly to models that use PHI are demonstrated.
De-identification of psychiatric intake records: Overview of 2016 CEGS N-GRID shared tasks Track 1.
De-identifying Hospital Discharge Summaries: An End-to-End Framework using Ensemble of Deep Learning Models
TLDR
An end-to-end deidentification framework to automatically remove PII from hospital discharge summaries is presented and it is shown that the ensemble model combined using the stacking Support Vector Machine method on the three base-models with the best F1 scores achieved excellent results.
Advancing the State of the Art in Clinical Natural Language Processing through Shared Tasks
TLDR
There is a clear trend in using data-driven methods to tackle problems in clinical NLP by highlighting the tasks, the most effective methodologies used, the data, and the sharing strategies.
PHICON: Improving Generalization of Clinical Text De-identification Models via Data Augmentation
TLDR
A simple yet effective data augmentation method PHICON, which creates augmented training corpora by replacing PHI entities with named-entities sampled from external sources, and by changing background context with synonym replacement or random word insertion, is proposed.
A study of deep learning methods for de-identification of clinical notes in cross-institute settings
TLDR
Fine-tuning is a potential solution to re-use pre-trained parameters and reduce the training time to customize deep learning-based de-identification models trained using clinical corpus from a different institution.
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References

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CRFs based de-identification of medical records
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The first de-identification system based on artificial neural networks (ANNs), which requires no handcrafted features or rules, unlike existing systems, is introduced, which outperforms the state-of-the-art systems.
Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1
De-identification of psychiatric intake records: Overview of 2016 CEGS N-GRID shared tasks Track 1.
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