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This paper describes novel models tailored for a new application, that ofextracting the symptoms mentioned in clinical conversations along with theirstatus. Lack of any publicly available corpus in this privacy-sensitive domainled us to develop our own corpus, consisting of about 3K conversationsannotated by professional medical scribes. We propose two novel deep learningapproaches to infer the symptom names and their status: (1) a new hierarchicalspan-attribute tagging (\SAT) model, trained using curriculum learning, and (2)a variant of sequence-to-sequence model which decodes the symptoms and theirstatus from a few speaker turns within a sliding window over the conversation.This task stems from a realistic application of assisting medical providers incapturing symptoms mentioned by patients from their clinical conversations. Toreflect this application, we define multiple metrics. From inter-rateragreement, we find that the task is inherently difficult. We conductcomprehensive evaluations on several contrasting conditions and observe thatthe performance of the models range from an F-score of 0.5 to 0.8 depending onthe condition. Our analysis not only reveals the inherent challenges of thetask, but also provides useful directions to improve the models.

Extracting Symptoms and their Status from Clinical Conversations