ROKS: NLP and ML to assist incident learning in radiation oncology

Radiation oncology incident learning tools are used in our clinics to proactively prevent potential radiation accidents and incidents. Clinical staff are encouraged to report minor incidents and often mandated to report major incidents in incident learning systems. Reported incidents are categorized and labelled according to radiation oncology-specific taxonomies designed to help find patterns that can facilitate follow-up actions to improve patient care and reduce the risk of incident recurrence. But the manual classification of such reports is a time-consuming and resource-intensive process and can hinder learning if not done correctly or in a timely manner. Therefore, strategies to reduce the burden of manual incident classification are of interest to the radiation oncology community. Therefore, this project aims to use Natural Language Processing (NLP) and Machine Learning (ML) techniques to automate the process of incident classification in real-time to help assist incident learning.

Felix Mathew
Felix Mathew
PhD Student
John Kildea
John Kildea
Assistant Professor of Medical Physics

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