ArticleApplied clinical informatics2024
Examining the Generalizability of Pretrained De-identification Transformer Models on Narrative Nursing Notes.
Article in Applied clinical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A Transformer-Based Pipeline for German Clinical Document De-Identification.Applied clinical informatics · 2025Article
- Toward Clinical Generative AI: Conceptual Framework.JMIR AI · 2024Article
- An Extensible Evaluation Framework Applied to Clinical Text Deidentification Natural Language Processing Tools: Multisystem and Multicorpus Study.Journal of medical Internet research · 2024Article
- Toward Identifying New Risk Aversions and Subsequent Limitations and Biases When Making De-identified Structured Data Sets Openly Available in a Post-LLM world.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
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5 authors.
Funding
Abstract
backgroundNarrative nursing notes are a valuable resource in informatics research with unique predictive signals about patient care. The open sharing of these data, however, is appropriately constrained by rigorous regulations set by the Health Insurance Portability and Accountability Act (HIPAA) for the protection of privacy. Several models have been developed and evaluated on the open-source i2b2 dataset. A focus on the generalizability of these models with respect to nursing notes remains understudied.
objectivesThe study aims to understand the generalizability of pretrained transformer models and investigate the variability of personal protected health information (PHI) distribution patterns between discharge summaries and nursing notes with a goal to inform the future design for model evaluation schema.
methodsTwo pretrained transformer models (RoBERTa, ClinicalBERT) fine-tuned on i2b2 2014 discharge summaries were evaluated on our data inpatient nursing notes and compared with the baseline performance. Statistical testing was deployed to assess differences in PHI distribution across discharge summaries and nursing notes.
resultsRoBERTa achieved the optimal performance when tested on an external source of data, with an F1 score of 0.887 across PHI categories and 0.932 in the PHI binary task. Overall, discharge summaries contained a higher number of PHI instances and categories of PHI compared with inpatient nursing notes.
conclusionThe study investigated the applicability of two pretrained transformers on inpatient nursing notes and examined the distinctions between nursing notes and discharge summaries concerning the utilization of personal PHI. Discharge summaries presented a greater quantity of PHI instances and types when compared with narrative nursing notes, but narrative nursing notes exhibited more diversity in the types of PHI present, with some pertaining to patient's personal life. The insights obtained from the research help improve the design and selection of algorithms, as well as contribute to the development of suitable performance thresholds for PHI.
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