Assessment of Medical Reports Uncertainty through Topic Modeling and Machine Learning

Abstract

Medical uncertainty is identified as one of the most important factors leading to miscommunication between health care providers and patients. Along with the rapid growth of Natural Language Processing and Machine Learning techniques, more opportunities to understand medical uncertainty became available, including quantifying and modeling medical uncertainty. In this work, we obtained more than 20,000 radiology teaching files as medical reports from multiple resources. Uncertainty ontologies were also obtained from two resources. We expanded the uncertainty term list by applying topic model Word2Vec to identify similar terms to original uncertainty ontologies. The teaching files were quantified into 5 uncertainty level classes by the sum of the Term Frequency - Inverse Document Frequency (TF-IDF) of the expanded uncertainty terms list. Results from topic modelling were used to produce features. The product of TF-IDF results of uncertainty terms in teaching files and the topic model results were then used to train classifiers to predict the uncertainty level in medical reports. Our exhaustive experimental analysis showed that Decision Tree can classify uncertainty level of medical reports at overall accuracy 82%, which is higher than K-nearest Neighbor (80%) and Naive Bayes (75%). The model can be used to identify medical reports' uncertainty level and limit miscommunication between parties and reduce diagnostic errors.

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