The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Photo of Mattias Ohlsson

Mattias Ohlsson

Professor

Photo of Mattias Ohlsson

Interpretable AI diagnostics for dyspnea in the emergency department by deep learning and a massive regional health care dataset

Author

  • Ellen Tolestam Heyman
  • Awais Ashfaq
  • Ulf Ekelund
  • Mattias Ohlsson
  • Jonas Björk
  • Lina Dahlén Holmqvist
  • Ardavan M. Khoshnood
  • Markus Lingman

Department/s

  • Emergency medicine
  • EpiHealth: Epidemiology for Health
  • LU Profile Area: Natural and Artificial Cognition
  • eSSENCE: The e-Science Collaboration
  • Artificial Intelligence in CardioThoracic Sciences (AICTS)
  • EPI@LUND
  • Surgery and public health
  • Cardiovascular Research - Hypertension

Publishing year

2023-05-31

Language

English

Document type

Poster

Topic

  • Cardiac and Cardiovascular Systems

Keywords

  • artificial intelligence
  • AI
  • Dyspnea
  • Artificiell intelligens
  • AI
  • Dyspne

Conference name

Swedish Emergency Medicine Talks - SWEETS23

Conference date

2023-05-31 - 2023-06-02

Conference place

Stockholm, Sweden

Status

Published

Project

  • Resource Management in the Emergency Department by using Machine Learning
  • AIR Lund - Artificially Intelligent use of Registers

Research group

  • Emergency medicine
  • Artificial Intelligence in CardioThoracic Sciences (AICTS)
  • EPI@LUND
  • Surgery and public health
  • Cardiovascular Research - Hypertension