Natural language processing and machine learning to assist radiation oncology incident learning

PURPOSE: To develop a Natural Language Processing (NLP) and Machine Learning (ML) pipeline that can be integrated into an Incident Learning System (ILS) to assist radiation oncology incident learning by semi‐automating incident classification. Our goal was to develop ML models that can generate label recommendations, arranged according to their likelihoods, for three data elements in Canadian NSIR‐RT taxonomy. METHODS: Over 6000 incident reports were gathered from the Canadian national ILS as well as our local ILS database. Incident descriptions from these reports were processed using various NLP techniques. The processed data with the expert‐generated labels were used to train and evaluate over 500 multi‐output ML algorithms. The top three models were identified and tuned for each of three different taxonomy data elements, namely: (1) process step where the incident occurred, (2) problem type of the incident and (3) the contributing factors of the incident. The best‐performing model after tuning was identified for each data element and tested on unseen data. RESULTS: The MultiOutputRegressor extended Linear SVR models performed best on the three data elements. On testing, our models ranked the most appropriate label 1.48 ± 0.03, 1.73 ± 0.05 and 2.66 ± 0.08 for process‐step, problem‐type and contributing factors respectively. CONCLUSIONS: We developed NLP‐ML models that can perform incident classification. These models will be integrated into our ILS to generate a drop‐down menu. This semi‐automated feature has the potential to improve the usability, accuracy and efficiency of our radiation oncology ILS.