Classification of patient case discussions through analysis of vocalisation graphs

Saturnino Luz*, Bridget Kane

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper investigates the use of amount and structure of talk as a basis for automatic classification of patient case discussions in multidisciplinary medical team meetings recorded in a real-world setting. We model patient case discussions as vocalisation graphs, building on research from the fields of interaction analysis and social psychology. These graphs are "content free" in that they only encode patterns of vocalisation and silence. The fact that it does not rely on automatic transcription makes the technique presented in this paper an attractive complement to more sophisticated speech processing methods as a means of indexing medical team meetings. We show that despite the simplicity of the underlying representation mechanism, accurate classification performance (F-scores: F-1 = 0.98, for medical patient case discussions, and F-1 = 0.97, for surgical case discussions) can be achieved with a simple k-nearest neighbour classifier when vocalisations are represented at the level of individual speakers. Possible applications of the method in health informatics for storage and retrieval of multimedia medical meeting records are discussed.

Original languageEnglish
Title of host publicationICMI-MLMI'09 - Proceedings of the International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interfaces
Pages107-114
Number of pages8
DOIs
Publication statusPublished - Nov 2009
EventInternational Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interfaces, ICMI-MLMI'09 - Cambridge, MA, United States
Duration: 2 Nov 20096 Nov 2009

Publication series

NameICMI-MLMI'09 - Proceedings of the International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interfaces

Conference

ConferenceInternational Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interfaces, ICMI-MLMI'09
CountryUnited States
CityCambridge, MA
Period2/11/096/11/09

Keywords

  • Electronic medical records
  • Language and action patterns
  • Medical team meetings
  • Patient Case Discussions

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