Design of an AI Support for Diagnosis of Dyspneic Adults at Time of Triage in the Emergency Department

Summary, in English

We created an AI support for diagnosis in dyspneic adults at time of triage in the emergency department.

Complete data from an entire regional health care system was analyzed, to find AI-derived, unknown, important diagnostic predictors. Most important were prior diagnoses of heart failure or COPD, daily smoking, atrial fibrillation/flutter, life difficulties and maternal care.

Sensitivity for AHF, eCOPD and pneumonia was 75%, 93%, and 54%, respectively, with a specificity above 75%.

Each patient visit received an individual graph with the AI´s underlying decision basis.

• Författare: Ellen Tolestam Heyman; Awais Ashfaq; Ulf Ekelund; Mattias Ohlsson; Jonas Björk; Ardavan M. Khoshnood; Markus Lingman
• Avdelning/ar: Akutsjukvård; EpiHealth: Epidemiology for Health; LU profilområde: Naturlig och artificiell kognition; eSSENCE: The e-Science Collaboration; Artificiell intelligens och thoraxkirurgisk vetenskap (AICTS); EPI@LUND; Kirurgi och folkhälsa; Kardiovaskulär forskning - hypertoni
• Publiceringsår: 2023-09
• Språk: Engelska
• Fulltext: - 1 MB; Download statistics
• Dokumenttyp: Affisch
• Ämne: Cardiac and Cardiovascular Systems
• Nyckelord: artificial intelligence; AI; Dyspnea; Artificiell intelligens; AI; Dyspne
• Conference name: European Emergency Medicine Congress 2023
• Conference date: 2023-09-17 - 2023-09-20
• Conference place: Barcelona, Spain
• Status: Published
• Projekt: Resource Management in the Emergency Department by using Machine Learning; AIR Lund - Artificially Intelligent use of Registers
• Forskningsgrupp: Emergency medicine; Artificial Intelligence in CardioThoracic Sciences (AICTS); EPI@LUND; Surgery and public health; Cardiovascular Research - Hypertension