Potential Applications of Machine Learning at Multidisciplinary Medical Team Meetings

Bridget Kane, Jing Su, Saturnino Luz

Research output: Contribution to conferencePaperpeer-review

Abstract

While machine learning (ML) systems have produced great advances in several domains, their use in support of complex cooperative work remains a research challenge. A particularly challenging setting, and one that may benefit from ML support is the work of multidisciplinary medical teams (MDTs). This paper focuses on the activities performed during the multidisciplinary medical team meeting (MDTM), reviewing their main characteristics in light of a longitudinal analysis of several MDTs in a large teaching hospital over a period of ten years and of our development of ML methods to support MDTMs, and identifying opportunities and possible pitfalls for the use of ML to support MDTMs.
Original languageEnglish
Publication statusPublished - 3 Nov 2019
EventThe 22nd ACM Conference on Computer-Supported Cooperative Work and Social Computing - Hilton Hotel, Austin, Austin, United States
Duration: 9 Nov 201913 Nov 2019
Conference number: 22
http://cscw.acm.org/2019/index.html

Conference

ConferenceThe 22nd ACM Conference on Computer-Supported Cooperative Work and Social Computing
Abbreviated titleCSCW 2019
Country/TerritoryUnited States
CityAustin
Period9/11/1913/11/19
Internet address

Keywords

  • cs.AI
  • cs.CY
  • cs.HC
  • cs.LG

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