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Towards objective and reproducible study of patient-doctor interaction : automatic text analysis based VR-CoDES annotation of consultation transcripts
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dc.contributor.author | Birkett, Charlotte | |
dc.contributor.author | Arandelovic, Ognjen | |
dc.contributor.author | Humphris, Gerald Michael | |
dc.date.accessioned | 2017-08-16T10:30:14Z | |
dc.date.available | 2017-08-16T10:30:14Z | |
dc.date.issued | 2017-07-11 | |
dc.identifier.citation | Birkett , C , Arandelovic , O & Humphris , G M 2017 , Towards objective and reproducible study of patient-doctor interaction : automatic text analysis based VR-CoDES annotation of consultation transcripts . in 2017 IEEE 39th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC) . , 8037399 , IEEE , pp. 2638-2641 , 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 , Jeju Island , Korea, Democratic People's Republic of , 11/07/17 . https://doi.org/10.1109/EMBC.2017.8037399 | en |
dc.identifier.citation | conference | en |
dc.identifier.other | PURE: 250828379 | |
dc.identifier.other | PURE UUID: 1f3f4ea5-2bb0-4a91-a7a2-5d8e8156fafb | |
dc.identifier.other | Scopus: 85032205128 | |
dc.identifier.other | ORCID: /0000-0002-4601-8834/work/64033917 | |
dc.identifier.other | WOS: 000427085303021 | |
dc.identifier.uri | https://hdl.handle.net/10023/11488 | |
dc.description.abstract | While increasingly appreciated for its importance,the interaction between health care professionals (HCP) and patients is notoriously difficult to study, with both methodological and practical challenges. The former has been addressed by the so-called Verona coding definitions of emotional sequences (VRCoDES)– a system for identifying and coding patient emotions and the corresponding HCP responses – shown to be reliable and informative in a number of independent studies in different health care delivery contexts. In the present work we focus on the practical challenge of the scalability of this coding system,namely on making it easily usable more widely and on applying it on larger patient cohorts. In particular, VR-CoDES is inherently complex and training is required to ensure consistent annotation of audio recordings or textual transcripts of consultations.Following up on our previous pilot investigation, in the present paper we describe the first automatic, computer based algorithm capable of providing coarse level coding of textual transcripts. We investigate different representations of patient utterances and classification methodologies, and label each utterance as either containing an explicit expression of emotional distress (a ‘concern’), an implicit one (a ‘cue’),or neither. Using a data corpus comprising 200 consultations between radiotherapists and adult female breast cancer patients we demonstrate excellent labelling performance. | |
dc.language.iso | eng | |
dc.publisher | IEEE | |
dc.relation.ispartof | 2017 IEEE 39th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC) | en |
dc.rights | © 2017, IEEE. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1109/EMBC.2017.8037399 | en |
dc.subject | QA75 Electronic computers. Computer science | en |
dc.subject | RC0254 Neoplasms. Tumors. Oncology (including Cancer) | en |
dc.subject | NDAS | en |
dc.subject | SDG 3 - Good Health and Well-being | en |
dc.subject.lcc | QA75 | en |
dc.subject.lcc | RC0254 | en |
dc.title | Towards objective and reproducible study of patient-doctor interaction : automatic text analysis based VR-CoDES annotation of consultation transcripts | en |
dc.type | Conference item | en |
dc.description.version | Postprint | en |
dc.contributor.institution | University of St Andrews. School of Computer Science | en |
dc.contributor.institution | University of St Andrews. School of Medicine | en |
dc.contributor.institution | University of St Andrews. WHO Collaborating Centre for International Child & Adolescent Health Policy | en |
dc.contributor.institution | University of St Andrews. Health Psychology | en |
dc.contributor.institution | University of St Andrews. St Andrews Sustainability Institute | en |
dc.identifier.doi | https://doi.org/10.1109/EMBC.2017.8037399 |
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