Forecasting the Acute Heart Failure Admissions: Development of Deep Learning Prediction Model Incorporating the Climate Information.
Acute heart failure
climate
deep learning
prediction model
Journal
Journal of cardiac failure
ISSN: 1532-8414
Titre abrégé: J Card Fail
Pays: United States
ID NLM: 9442138
Informations de publication
Date de publication:
10 Nov 2023
10 Nov 2023
Historique:
received:
05
09
2023
revised:
08
10
2023
accepted:
08
10
2023
pubmed:
13
11
2023
medline:
13
11
2023
entrez:
12
11
2023
Statut:
aheadofprint
Résumé
Climate is known to influence the incidence of cardiovascular events. However, their prediction with traditional statistical models remains imprecise. We analyzed 27,799 acute heart failure (AHF) admissions within the Tokyo CCU Network Database from January 2014 to December 2019. High-risk AHF (HR-AHF) day was defined as a day with the upper 10th percentile of AHF admission volume. Deep neural network (DNN) and traditional regression models were developed using the admissions in 2014-2018 and tested in 2019. Explanatory variables included 17 meteorological parameters. Shapley additive explanations were used to evaluate their importance. The median number of incidences of AHF was 12 (9-16) per day in 2014-2018 and 11 (9-15) per day in 2019. The predicted AHF admissions correlated well with the observed numbers (DNN: R The DNN model had good prediction ability for incident AHF using climate information. Forecasting AHF admissions could be useful for the effective management of AHF.
Sections du résumé
BACKGROUND
BACKGROUND
Climate is known to influence the incidence of cardiovascular events. However, their prediction with traditional statistical models remains imprecise.
METHODS AND RESULTS
RESULTS
We analyzed 27,799 acute heart failure (AHF) admissions within the Tokyo CCU Network Database from January 2014 to December 2019. High-risk AHF (HR-AHF) day was defined as a day with the upper 10th percentile of AHF admission volume. Deep neural network (DNN) and traditional regression models were developed using the admissions in 2014-2018 and tested in 2019. Explanatory variables included 17 meteorological parameters. Shapley additive explanations were used to evaluate their importance. The median number of incidences of AHF was 12 (9-16) per day in 2014-2018 and 11 (9-15) per day in 2019. The predicted AHF admissions correlated well with the observed numbers (DNN: R
CONCLUSIONS
CONCLUSIONS
The DNN model had good prediction ability for incident AHF using climate information. Forecasting AHF admissions could be useful for the effective management of AHF.
Identifiants
pubmed: 37952642
pii: S1071-9164(23)00866-7
doi: 10.1016/j.cardfail.2023.10.476
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2023 Elsevier Inc. All rights reserved.