Deep neural network models to automate incident triage in the radiation oncology incident learning system

Radiotherapy treatment for cancer patients involves a complex workflow involving radiation physicists, therapists, dosimetrists, physicians and nurses. Multiple hand-offs between these care team members often lead to errors varying in severity levels. Such errors are logged in incident reports stored in the Radiation Oncology Incident Learning System. Here, we present an automated incident triage and severity determination pipeline that can predict high and low severity incidents. Incident reports are collected from the US Veterans Health Affairs (VHA) and Virginia Commonwealth University (VCU) radiation oncology centers. Natural language processing (NLP) and deep learning (DL) methods, like CNN and BiLSTM, are used to predict severity using the 'Incident Description' information. Other features like 'Incident Type', 'Action taken by reporter' and 'Incident discovered at' are used to infer the best performing model. Random oversampling and minority class oversampling are employed to address large class imbalance ratios in the data.

We observed that CNN performs best on both VHA data (0.83 F1-score) and the combined VCU+VHA data (0.83 F1-score) while CNN with minority sampling performs better on VCU data (0.60 F1-score) using the 'Incident Description' feature. Different feature combinations suggest that the two feature model using 'Incident Description' and 'Action taken by reporter' performs better with CNN on both the VHA (0.84 F1-score) and combined VCU+VHA data (0.81 F1-score). Multiple features were considered for the first time where the two feature model using CNNs emerge as the best suited for automating the radiotherapy incident triage and prioritization process.