Extracting Structured Data from Physician-Patient ConversationsBy Predicting Noteworthy Utterances
Table 3: Aggregate results for the RoS abnormality prediction task. AN: predicted noteworthy utterances, RN: utterances predicted to be noteworthy specifically concerning a summary passage discussing review of systems, F2K: UMLS-extracted noteworthy utterances with added top predicted AN/RN utterances to get K total utterances, M-: macro average, m-: micro average
['[BOLD] Model', '[BOLD] Accuracy', '[BOLD] M-AUC', '[BOLD] M-F1', '[BOLD] m-AUC', '[BOLD] m-F1', '[BOLD] Precision-at-1']
Predicting RoS abnormality proves to be a more difficult task than predicting relevant diagnoses as reflected by the lower values achieved on all metrics. We hypothesize that this is because of the variety of symptoms that can be checked for each system. The cardiovascular system has 152 symptoms in our dataset, including ‘pain in the ribs’, ‘palpitations’, ‘increased heart rate’ and ‘chest ache’. A learning-based model would must learn to associate all of these symptoms with the cardiovascular system in addition to recognizing whether or not any given patient experiences the symptom.