Abstract
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 \(\sim \)71% accuracy.
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Notes
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This prevents overfitting and repetition when converting all the numbers to words.
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This can happen when a different form of a medication (e.g. abbreviation, generic or brand name) is used in the conversation compared to the annotation.
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Since we had high quality human written transcripts and our ASR transcripts did not contain spelling mistakes (as long as the word was correctly recognized), string matching worked well during testing.
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Acknowledgements
We thank: University of Pittsburgh Medical Center (UPMC) and Abridge AI Inc. for providing access to the de-identified data corpus; Dr. Shivdev Rao, CEO, Abridge AI Inc. and a practicing cardiologist in UPMC’s Heart and Vascular Institute, and Prlof. Florian Metze, Associate Research Professor, Carnegie Mellon University for helpful discussions; Ben Schloss, Steven Coleman, and Deborah Osakue for data business development and annotation management.
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Selvaraj, S.P., Konam, S. (2021). Medication Regimen Extraction from Medical Conversations. In: Shaban-Nejad, A., Michalowski, M., Buckeridge, D.L. (eds) Explainable AI in Healthcare and Medicine. Studies in Computational Intelligence, vol 914. Springer, Cham. https://doi.org/10.1007/978-3-030-53352-6_18
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