Abstract
Learning to interview patients to find out their disease is an essential part of the training of medical students. The practical part of this training has traditionally relied on paid actors that play the role of a patient to be interviewed. This process is expensive and severely limits the amount of practice per student. In this work, we present a novel data set and methods based on Natural Language Processing, for making progress towards modern applications and e-learning tools that support this training by providing language-based user interfaces with virtual patients. A data set of german transcriptions from live doctor-patient interviews was collected. These transcriptions are based on audio recordings of exercise sessions within the university and only the doctor’s utterances could be transcribed. We annotated each utterance with an intent inventory characterizing the purpose of the question or statement. For some intent classes, the data only contains a few samples, and we apply Information Retrieval and Deep Learning methods that are robust with respect to small amounts of training data for recognizing the intent of an utterance and providing the correct response. Our results show that the models are effective and they provide baseline performance scores on the data set for further research.- Anthology ID:
- 2020.lrec-1.88
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 702–709
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.88
- DOI:
- Cite (ACL):
- Robin Rojowiec, Benjamin Roth, and Maximilian Fink. 2020. Intent Recognition in Doctor-Patient Interviews. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 702–709, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Intent Recognition in Doctor-Patient Interviews (Rojowiec et al., LREC 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.lrec-1.88.pdf