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A novel interpretable deep learning model for diagnosis in emergency department dyspnoea patients based on complete data from an entire health care system

Heyman, Ellen T. LU orcid ; Ashfaq, Awais ; Ekelund, Ulf LU orcid ; Ohlsson, Mattias LU orcid ; Björk, Jonas LU orcid ; Khoshnood, Ardavan M. LU orcid and Lingman, Markus (2024) In PLoS ONE 19(12).
Abstract
Background 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” by using deep learning and complete, unselected data from an entire regional health care system. Methods In this cross-sectional study, we included all dyspnoeic ED visits of patients ≥ 18 years of age at the two EDs in the region of Halland, Sweden, 07/01/2017–12/31/2019. Data from the complete regional health care system within five years prior to the ED visit were analysed. Gold... (More)
Background 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” by using deep learning and complete, unselected data from an entire regional health care system. Methods In this cross-sectional study, we included all dyspnoeic ED visits of patients ≥ 18 years of age at the two EDs in the region of Halland, Sweden, 07/01/2017–12/31/2019. Data from the complete regional health care system within five years prior to the ED visit were analysed. Gold standard diagnoses were defined as the subsequent in-hospital or ED discharge notes, and a subsample was manually reviewed by emergency medicine experts. A novel deep learning model, the clinical attention-based recurrent encoder network (CareNet), was developed. Cohort performance was compared to a simpler CatBoost model. A list of all variables and their importance for diagnosis was created. For each unique patient visit, the model selected the most important variables, analysed them and presented them to the clinician interpretably by taking event time and clinical context into account. AUROC, sensitivity and specificity were compared. Findings The most prevalent diagnoses among the 10,315 dyspnoeic ED visits were AHF (15.5%), eCOPD (14.0%) and pneumonia (13.3%). Median number of unique events, i.e., registered clinical data with time stamps, per ED visit was 1,095 (IQR 459–2,310). CareNet median AUROC was 87.0%, substantially higher than the CatBoost model´s (81.4%). CareNet median sensitivity for AHF, eCOPD, and pneumonia was 74.5%, 92.6%, and 54.1%, respectively, with a specificity set above 75.0, slightly inferior to that of the CatBoost baseline model. The model assembled a list of 1,596 variables by importance for diagnosis, on top were prior diagnoses of heart failure or COPD, daily smoking, atrial fibrillation/flutter, life management difficulties and maternity care. Each patient visit received their own unique attention plot, graphically displaying important clinical events for the diagnosis. Interpretation We designed a novel interpretable deep learning model for diagnosis in emergency department dyspnoea patients by analysing unselected data from a complete regional health care system. (Less)
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author
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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Deep learning model, emergency department, emergency medicine, Dyspnea, artificial intellgience, machine learning
in
PLoS ONE
volume
19
issue
12
article number
e0311081
publisher
Public Library of Science (PLoS)
external identifiers
  • pmid:39729465
  • scopus:85213417112
ISSN
1932-6203
DOI
10.1371/journal.pone.0311081
project
AIR Lund - Artificially Intelligent use of Registers
Resource Management in the Emergency Department by using Machine Learning
language
English
LU publication?
yes
id
d1c5e99f-08b3-480c-b3c6-f7a6b01b4f55
date added to LUP
2024-12-28 01:58:34
date last changed
2025-06-01 08:57:32
@article{d1c5e99f-08b3-480c-b3c6-f7a6b01b4f55,
  abstract     = {{Background 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” by using deep learning and complete, unselected data from an entire regional health care system.   Methods In this cross-sectional study, we included all dyspnoeic ED visits of patients ≥ 18 years of age at the two EDs in the region of Halland, Sweden, 07/01/2017–12/31/2019. Data from the complete regional health care system within five years prior to the ED visit were analysed. Gold standard diagnoses were defined as the subsequent in-hospital or ED discharge notes, and a subsample was manually reviewed by emergency medicine experts. A novel deep learning model, the clinical attention-based recurrent encoder network (CareNet), was developed. Cohort performance was compared to a simpler CatBoost model. A list of all variables and their importance for diagnosis was created. For each unique patient visit, the model selected the most important variables, analysed them and presented them to the clinician interpretably by taking event time and clinical context into account. AUROC, sensitivity and specificity were compared.   Findings The most prevalent diagnoses among the 10,315 dyspnoeic ED visits were AHF (15.5%), eCOPD (14.0%) and pneumonia (13.3%). Median number of unique events, i.e., registered clinical data with time stamps, per ED visit was 1,095 (IQR 459–2,310). CareNet median AUROC was 87.0%, substantially higher than the CatBoost model´s (81.4%). CareNet median sensitivity for AHF, eCOPD, and pneumonia was 74.5%, 92.6%, and 54.1%, respectively, with a specificity set above 75.0, slightly inferior to that of the CatBoost baseline model. The model assembled a list of 1,596 variables by importance for diagnosis, on top were prior diagnoses of heart failure or COPD, daily smoking, atrial fibrillation/flutter, life management difficulties and maternity care. Each patient visit received their own unique attention plot, graphically displaying important clinical events for the diagnosis.   Interpretation We designed a novel interpretable deep learning model for diagnosis in emergency department dyspnoea patients by analysing unselected data from a complete regional health care system.}},
  author       = {{Heyman, Ellen T. and Ashfaq, Awais and Ekelund, Ulf and Ohlsson, Mattias and Björk, Jonas and Khoshnood, Ardavan M. and Lingman, Markus}},
  issn         = {{1932-6203}},
  keywords     = {{Deep learning model; emergency department; emergency medicine; Dyspnea; artificial intellgience; machine learning}},
  language     = {{eng}},
  month        = {{12}},
  number       = {{12}},
  publisher    = {{Public Library of Science (PLoS)}},
  series       = {{PLoS ONE}},
  title        = {{A novel interpretable deep learning model for diagnosis in emergency department dyspnoea patients based on complete data from an entire health care system}},
  url          = {{https://lup.lub.lu.se/search/files/203212113/journal.pone.0311081.pdf}},
  doi          = {{10.1371/journal.pone.0311081}},
  volume       = {{19}},
  year         = {{2024}},
}