ArticleBMC medical informatics and decision making2018
Leveraging text skeleton for de-identification of electronic medical records.
Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- OpenDeID Pipeline for Unstructured Electronic Health Record Text Notes Based on Rules and Transformers: Deidentification Algorithm Development and Validation Study.Journal of medical Internet research · 2023Article
- Combining External Medical Knowledge for Improving Obstetric Intelligent Diagnosis: Model Development and Validation.JMIR medical informatics · 2021Article
- Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation.Scientific reports · 2021Article
- Crosslingual named entity recognition for clinical de-identification applied to a COVID-19 Italian data set.Applied soft computing · 2020Article
- De-identification of Clinical Text via Bi-LSTM-CRF with Neural Language Models.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDe-identification is the first step to use these records for data processing or further medical investigations in electronic medical records. Consequently, a reliable automated de-identification system would be of high value.
methodsIn this paper, a method of combining text skeleton and recurrent neural network is proposed to solve the problem of de-identification. Text skeleton is the general structure of a medical record, which can help neural networks to learn better.
resultsWe evaluated our method on three datasets involving two English datasets from i2b2 de-identification challenge and a Chinese dataset we annotated. Empirical results show that the text skeleton based method we proposed can help the network to recognize protected health information.
conclusionsThe comparison between our method and state-of-the-art frameworks indicates that our method achieves high performance on the problem of medical record de-identification.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.