A novel interpretable deep learning model for diagnosis in emergency department dyspnoea patients ba...
A novel interpretable deep learning model for diagnosis in emergency department dyspnoea patients based on complete data from an entire health care system
About this item
Full title
Author / Creator
Heyman, Ellen T. , Ashfaq, Awais , Ekelund, Ulf , Ohlsson, Mattias , Björk, Jonas , Khoshnood, Ardavan M. , Lingman, Markus , Strategiska forskningsområden (SFO) , Naturvetenskapliga fakulteten , Surgery and public health , Section II , Department of Laboratory Medicine , EpiHealth: Epidemiology for Health , Computational Science for Health and Environment , Strategic research areas (SRA) , Thoraxkirurgi , Akutsjukvård , Department of Clinical Sciences, Lund , Lund University Profile areas , Lund University , Institutionen för laboratoriemedicin , Sektion V , Kirurgi och folkhälsa , Profile areas and other strong research environments , Section V , Medicin/akutsjukvård, Lund , Centrum för miljö- och klimatvetenskap (CEC) , Artificial Intelligence in CardioThoracic Sciences (AICTS) , Artificiell intelligens och thoraxkirurgisk vetenskap (AICTS) , Division of Occupational and Environmental Medicine, Lund University , Department of Clinical Sciences, Malmö , Faculty of Medicine , Medicinska fakulteten , Thoracic Surgery , Sektion II , Cardiovascular Research - Hypertension , Kardiovaskulär forskning - hypertoni , Medicine/Emergency Medicine, Lund , LU Profile Area: Natural and Artificial Cognition , Institutionen för kliniska vetenskaper, Lund , Institutionen för kliniska vetenskaper, Malmö , Avdelningen för arbets- och miljömedicin , Lunds universitet , Faculty of Science , Lunds universitets profilområden , Profilområden och andra starka forskningsmiljöer , Paediatrics (Lund) , Centre for Environmental and Climate Science (CEC) , Beräkningsvetenskap för hälsa och miljö , Pediatrik, Lund , LU profilområde: Naturlig och artificiell kognition , Emergency medicine , eSSENCE: The e-Science Collaboration and EPI@BIO
Publisher
United States: Public Library of Science
Journal title
Language
English
Formats
Publication information
Publisher
United States: Public Library of Science
Subjects
More information
Scope and Contents
Contents
Dyspnoea is one of the emergency department's (ED) most common and deadly chief complaints, but frequently misdiagnosed and mistreated. We aimed to design a diagnostic decision support which classifies dyspnoeic ED visits into acute heart failure (AHF), exacerbation of chronic obstructive pulmonary disease (eCOPD), pneumonia and "other diagnoses" b...
Alternative Titles
Full title
A novel interpretable deep learning model for diagnosis in emergency department dyspnoea patients based on complete data from an entire health care system
Authors, Artists and Contributors
Author / Creator
Ashfaq, Awais
Ekelund, Ulf
Ohlsson, Mattias
Björk, Jonas
Khoshnood, Ardavan M.
Lingman, Markus
Strategiska forskningsområden (SFO)
Naturvetenskapliga fakulteten
Surgery and public health
Section II
Department of Laboratory Medicine
EpiHealth: Epidemiology for Health
Computational Science for Health and Environment
Strategic research areas (SRA)
Thoraxkirurgi
Akutsjukvård
Department of Clinical Sciences, Lund
Lund University Profile areas
Lund University
Institutionen för laboratoriemedicin
Sektion V
Kirurgi och folkhälsa
Profile areas and other strong research environments
Section V
Medicin/akutsjukvård, Lund
Centrum för miljö- och klimatvetenskap (CEC)
Artificial Intelligence in CardioThoracic Sciences (AICTS)
Artificiell intelligens och thoraxkirurgisk vetenskap (AICTS)
Division of Occupational and Environmental Medicine, Lund University
Department of Clinical Sciences, Malmö
Faculty of Medicine
Medicinska fakulteten
Thoracic Surgery
Sektion II
Cardiovascular Research - Hypertension
Kardiovaskulär forskning - hypertoni
Medicine/Emergency Medicine, Lund
LU Profile Area: Natural and Artificial Cognition
Institutionen för kliniska vetenskaper, Lund
Institutionen för kliniska vetenskaper, Malmö
Avdelningen för arbets- och miljömedicin
Lunds universitet
Faculty of Science
Lunds universitets profilområden
Profilområden och andra starka forskningsmiljöer
Paediatrics (Lund)
Centre for Environmental and Climate Science (CEC)
Beräkningsvetenskap för hälsa och miljö
Pediatrik, Lund
LU profilområde: Naturlig och artificiell kognition
Emergency medicine
eSSENCE: The e-Science Collaboration
EPI@BIO
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_plos_journals_3149705065
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_plos_journals_3149705065
Other Identifiers
ISSN
1932-6203
E-ISSN
1932-6203
DOI
10.1371/journal.pone.0311081
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