Extracting Structured Data from Physician-Patient ConversationsBy Predicting Noteworthy Utterances
None
['System', 'Frequency']
We then simplified the medical problem tags by converting everything to lowercase, and removing elaborations given in parentheses. For example, we simplify “hypertension (moderate to severe)” to “hypertension”. For each of the 20 most frequent tags retrieved after the previous simplifications, we searched among all medical problems and added the ones that had the original tag as a substring. For example, “systolic hypertension” was merged into “hypertension”. Given a physician-patient conversation, we also predict the organ systems (e.g., respiratory system) for which the patient predicted a symptom (e.g., trouble breathing). During a patient’s visit, the physician conducts a Review of Systems (RoS), where the physician reviews organ systems and potential associated symptoms and asks if the patient is experiencing each symptom. In our dataset SOAP notes, the Subjective: Review of Systems subsection contains annotated observations from the RoS, each containing a system, symptom, and result. For instance, a system (e.g., “cardiovascular”), an associated symptom (e.g., “chest pain or discomfort”) and a result based on patient feedback (e.g., “confirms”, “denies”). To reduce sparsity in the data for system/symptom pairs, we consider only systems and whether or not each system contained a confirmed symptom.