Journal Article

Customization scenarios for de-identification of clinical notes

Szpektor, Idan, Dean, Jeff, Amira, Rony, Ellis, Scott, Bee, Gavin, Hassidim, Avinatan, Beryozkin, Genady, Po, Ming Jack, Corrado, Greg, Gilon, Oren, Hartman, Tzvika, Williams, Jutta, Matias, Yossi, Vainstein, Danny, Laish, Itay, Chou, Katherine, Slyper, Ronit, Howell, Michael D., Hoory, Shlomo

2020

Overview

Background Automated machine-learning systems are able to de-identify electronic medical records, including free-text clinical notes. Use of such systems would greatly boost the amount of data available to researchers, yet their deployment has been limited due to uncertainty about their performance when applied to new datasets. Objective We present practical options for clinical note de-identification, assessing performance of machine learning systems ranging from off-the-shelf to fully customized. Methods We implement a state-of-the-art machine learning de-identification system, training and testing on pairs of datasets that match the deployment scenarios. We use clinical notes from two i2b2 competition corpora, the Physionet Gold Standard corpus, and parts of the MIMIC-III dataset. Results Fully customized systems remove 97–99% of personally identifying information. Performance of off-the-shelf systems varies by dataset, with performance mostly above 90%. Providing a small labeled dataset or large unlabeled dataset allows for fine-tuning that improves performance over off-the-shelf systems. Conclusion Health organizations should be aware of the levels of customization available when selecting a de-identification deployment solution, in order to choose the one that best matches their resources and target performance level.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Automatic data collection systems / Automation / Clinical notes / Customization / Datasets / De-identification / Design and construction / Electronic health records / Electronic medical records / Electronic records / Free text / Health Informatics / Identification methods / Identification systems / Information systems / Information Systems and Communication Service / Learning algorithms / Machine learning / Management of Computing and Information Systems / Medical records / Medical research / Medicine / Medicine & Public Health / Methods / modeling / Natural language processing / Neural networks / Performance enhancement / Privacy / Recurrent neural networks / Research Article / Researchers / technology