Time series forecasting of weight for diuretic dose adjustment using bidirectional long short-term memory.
Clinician decision support system
Deep learning
Electronic medical records
Long Short-Term memory
Time series forecasting
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
31 Jul 2024
31 Jul 2024
Historique:
received:
14
06
2024
accepted:
26
07
2024
medline:
1
8
2024
pubmed:
1
8
2024
entrez:
31
7
2024
Statut:
epublish
Résumé
Loop diuretics are prevailing drugs to manage fluid overload in heart failure. However, adjusting to loop diuretic doses is strenuous due to the lack of a diuretic guideline. Accordingly, we developed a novel clinician decision support system for adjusting loop diuretics dosage with a Long Short-Term Memory (LSTM) algorithm using time-series EMRs. Weight measurements were used as the target to estimate fluid loss during diuretic therapy. We designed the TSFD-LSTM, a bi-directional LSTM model with an attention mechanism, to forecast weight change 48 h after heart failure patients were injected with loop diuretics. The model utilized 65 variables, including disease conditions, concurrent medications, laboratory results, vital signs, and physical measurements from EMRs. The framework processed four sequences simultaneously as inputs. An ablation study on attention mechanisms and a comparison with the transformer model as a baseline were conducted. The TSFD-LSTM outperformed the other models, achieving 85% predictive accuracy with MAE and MSE values of 0.56 and 1.45, respectively. Thus, the TSFD-LSTM model can aid in personalized loop diuretic treatment and prevent adverse drug events, contributing to improved healthcare efficacy for heart failure patients.
Identifiants
pubmed: 39085306
doi: 10.1038/s41598-024-68663-6
pii: 10.1038/s41598-024-68663-6
doi:
Substances chimiques
Diuretics
0
Sodium Potassium Chloride Symporter Inhibitors
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
17723Subventions
Organisme : Korea Medical Device Development Fund
ID : 1711195603
Organisme : Ministry of Health & Welfare, Republic of Korea
ID : HR20C0026
Informations de copyright
© 2024. The Author(s).
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