De-identification of free-text clinical notes
Name
1227276464-MIT.pdf
Size
796.27 KB
Format
Adobe PDF
Checksum (MD5)
cb01f77efd3ea5c35740f142edf4d808
Author(s)
Lin, Jing,M. Eng.Massachusetts Institute of Technology.
Advisor(s)
Alistair Johnson.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
Clinical notes contain rich information that is useful in medical research and investigation. However, clinical documents often contain explicit personal information that is protected by federal laws. Researchers are required to remove these personal identifiers before publicly release the notes, a process known as de-identification. In recent years, the healthcare community has initiated several competitions to expedite the development of automated de-identification systems. Notably, models built using recurrent neural networks achieved state-of-the-art performance on the de-identification task. Since the competition, new architectures based on transformers have been developed with excellent performance on general domain natural language processing tasks. Examples include BERT and RoBERTa. In this work, we evaluated de-identification using different choices of bidirectional transformer models and classifiers. Further, we developed a hybrid system that incorporates rule-based features into the bidirectional transformer model. Our results demonstrated state-of-the-art performance with an average 98.73% binary token F1 score, a 0.45% increase from current baseline models.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 71-73).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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