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3 April 2024 Use of natural language processing and semi-supervised machine learning to stratify STAT imaging studies by clinical urgency
Renaid B. Kim, L. Jordan Breyfogle, Benjamin Mervak, Lubomir Hadjiiski, Kenneth Buckwalter, Jessica G. Fried
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Proceedings Volume 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications; 1293106 (2024) https://doi.org/10.1117/12.3005687
Event: SPIE Medical Imaging, 2024, San Diego, California, United States
Abstract
The “STAT” designation for imaging studies is often overused and misused, obscuring the actual urgency of an imaging order. Not all STAT imaging orders are equal in terms of urgency, so we create semi-supervised machine learning models to classify actual urgency of the STAT imaging studies with more than 20,000 studies, even though only a small subset of data in the training set was manually labeled by the experts.
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Renaid B. Kim, L. Jordan Breyfogle, Benjamin Mervak, Lubomir Hadjiiski, Kenneth Buckwalter, and Jessica G. Fried "Use of natural language processing and semi-supervised machine learning to stratify STAT imaging studies by clinical urgency", Proc. SPIE 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications, 1293106 (3 April 2024); https://doi.org/10.1117/12.3005687
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KEYWORDS
Machine learning
Education and training
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