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

Abstract

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, problemtype 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.

SCORN: Sinter Composition Optimization with Regressive Convolutional Neural Network

“…The running time of training our model is 350 s, and the inference time is less than 0.1 s. To train the DecisionTree model, we used the default parameters from Python's Scikit-Learn module. For SVR, the algorithm does not support multiple outputs for regression problems, and we implemented multi-objective support vector regression via a correlation regression chain [44,45]. We used the RBF (radial basis function kernel) kernel, and other parameters were set as default values.…”

Natural Language Processing – Finding the Missing Link for Oncologic Data, 2022

“…In an interesting study with potential applicability to other areas, Mathew et al developed NLP-ML models for incident classification in radiation oncology. They integrated these into the incident learning system to generate a drop-down menu such that the model as a semi-automated feature could improve the usability, accuracy and efficiency of the incident reporting system overall [101]. Hong et al had two independent reviewers identify National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) v5.0 symptoms from 100 randomly selected notes for on-treatment visits during radiation therapy with adjudication by a third reviewer.…”

Automated Error Labeling in Radiation Oncology via Statistical Natural Language Processing

“…Research on using NLP for medical errors in radiation oncology has received limited attention, although recent work shows promise in developing models that are effective in error labeling/classification [ 13 , 14 ]. Previous work has been focused on safety assurance and error reduction, but few studies have incorporated NLP into their methodology.…”