ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2017
Leveraging existing corpora for de-identification of psychiatric notes using domain adaptation.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Enhancing Cross-Domain Generalizability in Social Determinants of Health Extraction with Prompt-Tuning Large Language Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Improving Model Transferability for Clinical Note Section Classification Models Using Continued Pretraining.medRxiv : the preprint server for health sciences · 2023Article
- Ensemble Approaches to Recognize Protected Health Information in Radiology Reports.Journal of digital imaging · 2022Article
- Biomedical Ontologies to Guide AI Development in Radiology.Journal of digital imaging · 2021Review
- Transferability of neural network clinical deidentification systems.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Improving domain adaptation in de-identification of electronic health records through self-training.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- A Review of Recent Work in Transfer Learning and Domain Adaptation for Natural Language Processing of Electronic Health Records.Yearbook of medical informatics · 2021Review
- Evaluation of Automated Public De-Identification Tools on a Corpus of Radiology Reports.Radiology. Artificial intelligence · 2020Article
- Customization scenarios for de-identification of clinical notes.BMC medical informatics and decision making · 2020Article
- Adapting Word Embeddings from Multiple Domains to Symptom Recognition from Psychiatric Notes.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018Article
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Authors and funding
4 authors.
Funding
Abstract
De-identification of clinical notes is a special case of named entity recognition. Supervised machine-learning (ML) algorithms have achieved promising results for this task. However, ML-based de-identification systems often require annotating a large number of clinical notes of interest, which is costly. Domain adaptation (DA) is a technology that enables learning from annotated datasets from different sources, thereby reducing annotation cost required for ML training in the target domain. In this study, we investigate the use of DA methods for deidentification of psychiatric notes. Three state-of-the-art DA methods: instance pruning, instance weighting, and feature augmentation are applied to three source corpora of annotated hospital discharge summaries, outpatient notes, and a mixture of different note types written for diabetic patients. Our results show that DA can increase deidentification performance over the baselines, indicating that it can effectively reduce annotation cost for the target psychiatric notes. Feature augmentation is shown to increase performance the most among the three DA methods. Performance variation among the different types of clinical notes is also observed, showing that a mixture of different types of notes brings the biggest increase in performance.
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29854175PMC5977650What OpenQuestion holds
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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.