Scott Ellis
Scott Ellis is a Product Manager on Google’s Security & Privacy team focusing on data privacy technology for Google Cloud Platform. Scott received a BS in Chemistry from the University of California, San Diego and now lives in the Bay Area with his wife and two children.
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Customization Scenarios for De-identification of Clinical Notes
Danny Vainstein
Gavin Edward Bee
Jack Po
Jutta Williams
Kat Chou
Ronit Yael Slyper
Rony Amira
Shlomo Hoory
Tzvika Hartman
BMC Medical Informatics and Decision Making (2020)
Preview abstract
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.
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