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13 novembre 2017
- handle: 10670/1.0vgcac
- hal: hal-01654812
- doi: 10.1145/3136755.3136816
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info:eu-repo/semantics/altIdentifier/doi/10.1145/3136755.3136816
info:eu-repo/semantics/OpenAccess
Mots-clés
Virtual reality multimodal corpora Embodied conversational agent rules mining feedbacks health domain CCS CONCEPTS• Human-centered computing → HCI design and evaluation methods; Virtual reality; Natural language interfaces• Computing methodologies → Rule learningSujets proches
Doctors Doctors of medicine MDs (Physicians) Medical doctors Allopathic doctors Medical professionCiter ce document
Chris Porhet et al., « Mining a Multimodal Corpus of Doctor's Training for Virtual Patient's Feedbacks », HAL-SHS : linguistique, ID : 10.1145/3136755.3136816
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Résumé
Doctors should be trained not only to perform medical or surgical acts but also to develop competences in communication for their interaction with patients. For instance, the way doctors deliver bad news has a signifcant impact on the therapeutic process. In order to facilitate the doctors' training to break bad news, we aim at developing a virtual patient ables to interact in a multimodal way with doctors announcing an undesirable event. One of the key elements to create an engaging interaction is the feedbacks' behavior of the virtual character. In order to model the virtual patient's feedbacks in the context of breaking bad news, we have analyzed a corpus of real doctor's training. Te verbal and nonverbal signals of both the doctors and the patients have been annotated. In order to identify the types of feedbacks and the elements that may elicit a feedback, we have explored the corpus based on sequences mining methods. Rules, that have been extracted from the corpus, enable us to determine when a virtual patient should express which feedbacks when a doctor announces a bad new.