Detecting conversation topics in primary care office visits from transcripts of patient-provider interactions
Journal of the American Medical Informatics Association2019Vol. 26(12), pp. 1493–1504
Citations Over TimeTop 10% of 2019 papers
Jihyun Park, Dimitrios Kotzias, Patty Kuo, Robert L. Logan, Kritzia Merced, Sameer Singh, Michael Tanana, Efi Karra Taniskidou, Jennifer Elston Lafata, David C. Atkins, Ming Tai-Seale, Zac E. Imel, Padhraic Smyth
Abstract
Incorporating sequential information across talk-turns improves the accuracy of topic prediction in patient-provider dialog by smoothing out noisy information from talk-turns. Although the results are promising, more advanced prediction techniques and larger labeled datasets will likely be required to achieve prediction performance appropriate for real-world clinical applications.
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