Impact of De-Identification on Clinical Text Classification Using Traditional and Deep Learning Classifiers
| Autor: | Obeid, Jihad S., Heider, Paul M., Weeda, Erin R., Matuskowitz, Andrew J., Carr, Christine M., Gagnon, Kevin, Crawford, Tami, Meystre, Stephane M. |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Studies in health technology and informatics |
| ISSN: | 1879-8365 0926-9630 |
| Popis: | Clinical text de-identification enables collaborative research while protecting patient privacy and confidentiality; however, concerns persist about the reduction in the utility of the de-identified text for information extraction and machine learning tasks. In the context of a deep learning experiment to detect altered mental status in emergency department provider notes, we tested several classifiers on clinical notes in their original form and on their automatically de-identified counterpart. We tested both traditional bag-of-words based machine learning models as well as word-embedding based deep learning models. We evaluated the models on 1,113 history of present illness notes. A total of 1,795 protected health information tokens were replaced in the de-identification process across all notes. The deep learning models had the best performance with accuracies of 95% on both original and de-identified notes. However, there was no significant difference in the performance of any of the models on the original vs. the de-identified notes. |
| Databáze: | OpenAIRE |
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