  Extracting relevant information from medical conversations and providing it
to doctors and patients might help in addressing doctor burnout and patient
forgetfulness. In this paper, we focus on extracting the Medication Regimen
(dosage and frequency for medications) discussed in a medical conversation. We
frame the problem as a Question Answering (QA) task and perform comparative
analysis over: a QA approach, a new combined QA and Information Extraction
approach, and other baselines. We use a small corpus of 6,692 annotated
doctor-patient conversations for the task. Clinical conversation corpora are
costly to create, difficult to handle (because of data privacy concerns), and
thus scarce. We address this data scarcity challenge through data augmentation
methods, using publicly available embeddings and pretrain part of the network
on a related task (summarization) to improve the model's performance. Compared
to the baseline, our best-performing models improve the dosage and frequency
extractions' ROUGE-1 F1 scores from 54.28 and 37.13 to 89.57 and 45.94,
respectively. Using our best-performing model, we present the first fully
automated system that can extract Medication Regimen tags from spontaneous
doctor-patient conversations with about $\approx$71% accuracy.

Computation and Language;Information Retrieval;Machine Learning