ArticleProceedings. IEEE International Conference on Healthcare Informatics2019
A Study of Deep Learning Methods for De-identification of Clinical Notes at Cross Institute Settings.
Article in Proceedings. IEEE International Conference on Healthcare Informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed, 20 citations in OpenAlex.
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
In this study, we examined a deep learning method for de-identification of clinical notes at UF Health under a cross-institute setting. We developed deep learning models using 2014 i2b2/UTHealth corpus and evaluated the performance using clinical notes collected from UF Health. We compared four pre-trained word embeddings, including two embeddings from the general domain and two embeddings from the clinical domain. We also explored linguistic features (i.e., word shape and part-of-speech) to further improve the performance of de-identification. The experimental results show that the performance of deep learning models trained using i2b2/UTHealth corpus significantly dropped (strict and relax F1 scores dropped from 0.9547 and 0.9646 to 0.8360 and 0.8870) when applied to another corpus from a different institution (UF Health). Linguistic features, including word shapes and part-of-speech, could further improve the performance of de-identification in cross-institute settings (improved to 0.8527 and 0.9052).
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Registered trials
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