Extracting Symptoms and their Status from Clinical Conversations

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This paper describes novel models tailored for a new application, that of extracting the symptoms mentioned in clinical conversations along with their status.

• Lack of any publicly available corpus in this privacy-sensitive domain led us to develop our own corpus, consisting of about 3K conversations annotated by professional medical scribes.
• We propose two novel deep learning approaches to infer the symptom names and their status: (1) a new hierarchical span-attribute tagging (\SAT) model, trained using curriculum learning, and (2) a variant of sequence-to-sequence model which decodes the symptoms and their status from a few speaker turns within a sliding window over the conversation.
• This task stems from a realistic application of assisting medical providers in capturing symptoms mentioned by patients from their clinical conversations.