Accepted for/Published in: Interactive Journal of Medical Research
Date Submitted: Feb 8, 2023
Date Accepted: Jul 24, 2023
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DEFT: a web-based system for DE-identifying Free Text data in electronic medical records using human in the loop deep learning
ABSTRACT
Background:
The valuable narrative free text in Electronic Medical Records (EMRs) must be de-identified by removing Personally Identifiable Information (PII) before releasing it for secondary use. Manual de-identification is time-consuming and labour-intensive. Existing de-identification systems have a steep learning curve.
Objective:
We sought to develop an accurate, web-based system for de-identifying free text in EMRs, which can be readily and easily adopted in real-world settings.
Methods:
DEFT was designed with the goals of easy adoption and rapid and secure de-identification at high accuracy. It provides a simple and task-focused web user interface for users to easily perform the de-identification work. An interactive learning loop powered by a state-of-the-art deep learning model is integrated into DEFT to speed up the de-identification process and increase its performance over time.
Results:
DEFT has advantages over existing systems in terms of its support for project management, user access control, data management, and an interactive learning process. In a real-world use case of de-identifying clinical notes, which were extracted from one referral hospital in Sydney, Australia, DEFT achieved a high F1 score of 95.07% using 600 annotated clinical notes.
Conclusions:
The DEFT system can be rapidly deployed for de-identifying free text in EMRs. End users with minimal technical knowledge can perform the de-identification work with only a shallow learning curve.
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