Re-examination of Rule-Based Methods in Deidentification of Electronic Health Records: Algorithm Development and Validation

Zhao, Zhenyu; Yang, Muyun; Tang, Buzhou; Zhao, Tiejun

doi:10.2196/17622

Cited by 4 publications

(3 citation statements)

References 21 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deidentification of clinical records, as an application, is a critical step in the use of electronic health records for academic research. Zhao et al [ 16 ] investigated the usefulness of rule-based learners in a hybrid deidentification system. A data-driven rule learner named transformation-based error-driven learning was integrated into a hybrid system.…”

Section: Resultsmentioning

confidence: 99%

Health Natural Language Processing: Methodology Development and Applications

Hao¹,

Huang²,

Liang³

et al. 2021

JMIR Med Inform

Self Cite

View full text Add to dashboard Cite

With the rapid growth of information technology, the necessity for processing substantial amounts of health data using advanced information technologies is increasing. A large amount of valuable data exists in natural text such as diagnosis text, discharge summaries, online health discussions, and eligibility criteria of clinical trials. Health natural language processing, as an interdisciplinary field of natural language processing and health care, plays a substantial role in a wide scope of both methodology development and applications. This editorial shares the most recent methodology innovations of health natural language processing and applications in the medical domain published in this JMIR Medical Informatics special theme issue entitled "Health Natural Language Processing: Methodology Development and Applications".

show abstract

Section: Resultsmentioning

confidence: 99%

Health Natural Language Processing: Methodology Development and Applications

Hao¹,

Huang²,

Liang³

et al. 2021

JMIR Med Inform

Self Cite

View full text Add to dashboard Cite

show abstract

“…Sweeney [ 10 ] proposed another rule-based approach for identifying and replacing SHI.MIT-De-id (PhysioNet) software, which is also a rule-based deidentification system developed using nursing progress reports [ 11 ]. Zhao et al [ 12 ] developed a rule-based model and integrated it into an ensemble framework and achieved the best performance of the model when compared with a non–rule-based model. Similarly, Dehghan et al [ 13 ] developed cDeID software to deidentify 7 HIPAA categories.…”

Section: Introductionmentioning

confidence: 99%

OpenDeID Pipeline for Unstructured Electronic Health Record Text Notes Based on Rules and Transformers: Deidentification Algorithm Development and Validation Study

Liu,

Gupta,

Chen

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

Background Electronic health records (EHRs) in unstructured formats are valuable sources of information for research in both the clinical and biomedical domains. However, before such records can be used for research purposes, sensitive health information (SHI) must be removed in several cases to protect patient privacy. Rule-based and machine learning–based methods have been shown to be effective in deidentification. However, very few studies investigated the combination of transformer-based language models and rules. Objective The objective of this study is to develop a hybrid deidentification pipeline for Australian EHR text notes using rules and transformers. The study also aims to investigate the impact of pretrained word embedding and transformer-based language models. Methods In this study, we present a hybrid deidentification pipeline called OpenDeID, which is developed using an Australian multicenter EHR-based corpus called OpenDeID Corpus. The OpenDeID corpus consists of 2100 pathology reports with 38,414 SHI entities from 1833 patients. The OpenDeID pipeline incorporates a hybrid approach of associative rules, supervised deep learning, and pretrained language models. Results The OpenDeID achieved a best F1-score of 0.9659 by fine-tuning the Discharge Summary BioBERT model and incorporating various preprocessing and postprocessing rules. The OpenDeID pipeline has been deployed at a large tertiary teaching hospital and has processed over 8000 unstructured EHR text notes in real time. Conclusions The OpenDeID pipeline is a hybrid deidentification pipeline to deidentify SHI entities in unstructured EHR text notes. The pipeline has been evaluated on a large multicenter corpus. External validation will be undertaken as part of our future work to evaluate the effectiveness of the OpenDeID pipeline.

show abstract

“…In the past few decades, rule-based [ 6 , 7 ] and machine learning–based [ 3 , 8 , 9 ] approaches have been the mainstream approaches to identifying entities in sentences or documents. Rule-based methods utilize special semantic dictionaries to establish a set of regular expressions [ 4 , 5 ] to extract PHI from EHRs.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Method for Deidentifying Protected Health Information in Chinese Electronic Health Records: Algorithm Development and Validation

Wang¹,

Li²,

Yang³

et al. 2022

JMIR Med Inform

View full text Add to dashboard Cite

Background With the popularization of electronic health records in China, the utilization of digitalized data has great potential for the development of real-world medical research. However, the data usually contains a great deal of protected health information and the direct usage of this data may cause privacy issues. The task of deidentifying protected health information in electronic health records can be regarded as a named entity recognition problem. Existing rule-based, machine learning–based, or deep learning–based methods have been proposed to solve this problem. However, these methods still face the difficulties of insufficient Chinese electronic health record data and the complex features of the Chinese language. Objective This paper proposes a method to overcome the difficulties of overfitting and a lack of training data for deep neural networks to enable Chinese protected health information deidentification. Methods We propose a new model that merges TinyBERT (bidirectional encoder representations from transformers) as a text feature extraction module and the conditional random field method as a prediction module for deidentifying protected health information in Chinese medical electronic health records. In addition, a hybrid data augmentation method that integrates a sentence generation strategy and a mention-replacement strategy is proposed for overcoming insufficient Chinese electronic health records. Results We compare our method with 5 baseline methods that utilize different BERT models as their feature extraction modules. Experimental results on the Chinese electronic health records that we collected demonstrate that our method had better performance (microprecision: 98.7%, microrecall: 99.13%, and micro-F1 score: 98.91%) and higher efficiency (40% faster) than all the BERT-based baseline methods. Conclusions Compared to baseline methods, the efficiency advantage of TinyBERT on our proposed augmented data set was kept while the performance improved for the task of Chinese protected health information deidentification.

show abstract

Re-examination of Rule-Based Methods in Deidentification of Electronic Health Records: Algorithm Development and Validation

Cited by 4 publications

References 21 publications

Health Natural Language Processing: Methodology Development and Applications

Health Natural Language Processing: Methodology Development and Applications

OpenDeID Pipeline for Unstructured Electronic Health Record Text Notes Based on Rules and Transformers: Deidentification Algorithm Development and Validation Study

An Efficient Method for Deidentifying Protected Health Information in Chinese Electronic Health Records: Algorithm Development and Validation

Contact Info

Product

Resources

About