The nonverbal structure of patient case discussions in multidisciplinary medical team meetings
| Autor: | Saturnino Luz |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Knowledge management
Audio analysis business.industry Computer science Business Management and Accounting(all) Search engine indexing General Business Management and Accounting Multidisciplinary medical team meetings Computer Science Applications Task (project management) Market segmentation Multidisciplinary approach Content analysis Computer-supported cooperative work Meeting analysis Segmentation Search of spontaneous speech Duration (project management) business Dialogue segmentation Information Systems |
| Zdroj: | Luz, S 2012, ' The non-Verbal Structure of Patient Case Discussions in Multidisciplinary Medical Team Meetings ', ACM Transactions on Information Systems, vol. 30, no. 3, 17, pp. 17:1-17:24 . https://doi.org/10.1145/2328967.2328970 |
| ISSN: | 1558-2868 1046-8188 |
| DOI: | 10.1145/2328967.2328970 |
| Popis: | Meeting analysis has a long theoretical tradition in social psychology, with established practical ramifications in computer science, especially in computer supported cooperative work. More recently, a good deal of research has focused on the issues of indexing and browsing multimedia records of meetings. Most research in this area, however, is still based on data collected in laboratories, under somewhat artificial conditions. This article presents an analysis of the discourse structure and spontaneous interactions at real-life multidisciplinary medical team meetings held as part of the work routine in a major hospital. It is hypothesized that the conversational structure of these meetings, as indicated by sequencing and duration of vocalizations, enables segmentation into individual patient case discussions. The task of segmenting audio-visual records of multidisciplinary medical team meetings is described as a topic segmentation task, and a method for automatic segmentation is proposed. An empirical evaluation based on hand labelled data is presented, which determines the optimal length of vocalization sequences for segmentation, and establishes the competitiveness of the method with approaches based on more complex knowledge sources. The effectiveness of Bayesian classification as a segmentation method, and its applicability to meeting segmentation in other domains are discussed. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|