Machine learning glucose forecasting models for septic patients Article Swipe
Jiang He
,
Hua Jiang
,
Qi Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-23057-0
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-23057-0
Sepsis-induced glucose fluctuations present major challenges in critical care, underscoring the importance of accurate glucose monitoring and forecasting to improve patient outcomes. This study introduces a suite of forecasting models trained using continuous glucose monitoring data from a diabetic patient with sepsis (19,621 data points). The models include four transformer-based ones (iTransformer, Crossformer, PatchTST, FEDformer), a dynamic linear model (DLinear), and an ensemble zero-shot inference method leveraging ChatGPT-4. Model performance was evaluated for 15-, 30-, and 60-minute prediction horizons with an optimized 30-minute lookback window. PatchTST achieved the lowest mean maximum percentage error (MMPE) for short-term forecasts (3.0% at 15 minutes), while DLinear excelled at longer horizons (7.46% and 14.41% MMPE at 30 and 60 minutes, respectively). The ensemble ChatGPT-4 approach also showed competitive results. Overall, this work offers a toolbox of advanced forecasting models for ICU glucose prediction and management. The comprehensive comparison among the models highlights the promise of machine learning models-particularly DLinear and PatchTST-in supporting glucose monitoring and ultimately digital twin implementations, paving the way toward personalized and adaptive glycemic control in septic patients.
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Metadata
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- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-025-23057-0
- https://www.nature.com/articles/s41598-025-23057-0.pdf
- OA Status
- gold
- References
- 45
- OpenAlex ID
- https://openalex.org/W4416333236
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4416333236Canonical identifier for this work in OpenAlex
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https://doi.org/10.1038/s41598-025-23057-0Digital Object Identifier
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Machine learning glucose forecasting models for septic patientsWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-11-18Full publication date if available
- Authors
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Jiang He, Hua Jiang, Qi WangList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-025-23057-0Publisher landing page
- PDF URL
-
https://www.nature.com/articles/s41598-025-23057-0.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41598-025-23057-0.pdfDirect OA link when available
- Cited by
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0Total citation count in OpenAlex
- References (count)
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45Number of works referenced by this work
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| abstract_inverted_index.septic | 175 |
| abstract_inverted_index.showed | 122 |
| abstract_inverted_index.toward | 168 |
| abstract_inverted_index.(19,621 | 42 |
| abstract_inverted_index.DLinear | 102, 154 |
| abstract_inverted_index.control | 173 |
| abstract_inverted_index.digital | 162 |
| abstract_inverted_index.dynamic | 56 |
| abstract_inverted_index.glucose | 1, 14, 33, 137, 158 |
| abstract_inverted_index.improve | 19 |
| abstract_inverted_index.include | 47 |
| abstract_inverted_index.machine | 151 |
| abstract_inverted_index.maximum | 90 |
| abstract_inverted_index.patient | 20, 39 |
| abstract_inverted_index.present | 3 |
| abstract_inverted_index.promise | 149 |
| abstract_inverted_index.toolbox | 130 |
| abstract_inverted_index.trained | 30 |
| abstract_inverted_index.window. | 84 |
| abstract_inverted_index.Overall, | 125 |
| abstract_inverted_index.PatchTST | 85 |
| abstract_inverted_index.accurate | 13 |
| abstract_inverted_index.achieved | 86 |
| abstract_inverted_index.adaptive | 171 |
| abstract_inverted_index.advanced | 132 |
| abstract_inverted_index.approach | 120 |
| abstract_inverted_index.critical | 7 |
| abstract_inverted_index.diabetic | 38 |
| abstract_inverted_index.ensemble | 62, 118 |
| abstract_inverted_index.excelled | 103 |
| abstract_inverted_index.glycemic | 172 |
| abstract_inverted_index.horizons | 78, 106 |
| abstract_inverted_index.learning | 152 |
| abstract_inverted_index.lookback | 83 |
| abstract_inverted_index.minutes, | 115 |
| abstract_inverted_index.points). | 44 |
| abstract_inverted_index.results. | 124 |
| abstract_inverted_index.30-minute | 82 |
| abstract_inverted_index.60-minute | 76 |
| abstract_inverted_index.ChatGPT-4 | 119 |
| abstract_inverted_index.PatchTST, | 53 |
| abstract_inverted_index.evaluated | 71 |
| abstract_inverted_index.forecasts | 96 |
| abstract_inverted_index.inference | 64 |
| abstract_inverted_index.minutes), | 100 |
| abstract_inverted_index.optimized | 81 |
| abstract_inverted_index.outcomes. | 21 |
| abstract_inverted_index.patients. | 176 |
| abstract_inverted_index.zero-shot | 63 |
| abstract_inverted_index.(DLinear), | 59 |
| abstract_inverted_index.ChatGPT-4. | 67 |
| abstract_inverted_index.challenges | 5 |
| abstract_inverted_index.comparison | 143 |
| abstract_inverted_index.continuous | 32 |
| abstract_inverted_index.highlights | 147 |
| abstract_inverted_index.importance | 11 |
| abstract_inverted_index.introduces | 24 |
| abstract_inverted_index.leveraging | 66 |
| abstract_inverted_index.monitoring | 15, 34, 159 |
| abstract_inverted_index.percentage | 91 |
| abstract_inverted_index.prediction | 77, 138 |
| abstract_inverted_index.short-term | 95 |
| abstract_inverted_index.supporting | 157 |
| abstract_inverted_index.ultimately | 161 |
| abstract_inverted_index.FEDformer), | 54 |
| abstract_inverted_index.PatchTST-in | 156 |
| abstract_inverted_index.competitive | 123 |
| abstract_inverted_index.forecasting | 17, 28, 133 |
| abstract_inverted_index.management. | 140 |
| abstract_inverted_index.performance | 69 |
| abstract_inverted_index.Crossformer, | 52 |
| abstract_inverted_index.fluctuations | 2 |
| abstract_inverted_index.personalized | 169 |
| abstract_inverted_index.underscoring | 9 |
| abstract_inverted_index.comprehensive | 142 |
| abstract_inverted_index.(iTransformer, | 51 |
| abstract_inverted_index.Sepsis-induced | 0 |
| abstract_inverted_index.respectively). | 116 |
| abstract_inverted_index.implementations, | 164 |
| abstract_inverted_index.transformer-based | 49 |
| abstract_inverted_index.models-particularly | 153 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |