ArticleComputational and structural biotechnology journal2025
Enhancing glucose level prediction of ICU patients through hierarchical modeling of irregular time-series.
Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- An Algorithm for Identifying Microvascular Complications in Type 1 Diabetes in the Clinical Setting: Development and External Validation.Health data science · 2026Article
- Cyclic dual latent discovery for improved blood glucose prediction through patient-provider interaction modeling: a prediction study.Ewha medical journal · 2025Article
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Authors and funding
3 authors.
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Abstract
Accurately predicting blood glucose (BG) levels of ICU patients is critical, as both hypoglycemia (BG < 70 mg/dL) and hyperglycemia (BG > 180 mg/dL) are associated with increased morbidity and mortality. This study presents a proof-of-concept machine learning framework, the Multi-source Irregular Time-Series Transformer (MITST), designed to predict BG levels in ICU patients. In contrast to existing methods that rely heavily on manual feature engineering or utilize limited Electronic Health Record (EHR) data sources, MITST integrates diverse clinical data-including laboratory results, medications, and vital signs-without predefined aggregation. The model leverages a hierarchical Transformer architecture, designed to capture interactions among features within individual timestamps, temporal dependencies across different timestamps, and semantic relationships across multiple data sources. Evaluated using the extensive eICU database (200,859 ICU stays across 208 hospitals), MITST achieves a statistically significant (
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