Multimodal convolutional neural networks for the prediction of acute kidney injury in the intensive care
Open accessAbstract
Increased monitoring of health-related data for ICU patients holds great potential for the early prediction of medical outcomes. Research on whether the use of clinical notes and concepts from knowledge bases can improve the performance of prediction models is limited. We investigated the effects of combining clinical variables, clinical notes, and clinical concepts. We focus on the early prediction of Acute Kidney Injury (AKI) in the intensive care unit (ICU). AKI is a sudden reduction in kidney function measured by increased serum creatinine (SCr) or decreased urine output. AKI may occur in up to 30% of ICU stays. We developed three models based on convolutional neural networks using data from the Medical Information Mart for Intensive Care (MIMIC) database. The models used clinical variables, free-text notes, and concepts from the Elsevier H-Graph. Our models achieved good predictive performance (AUROC 0.73-0.90). These models were assessed both when using Scr and urine output as predictors and when omitting them. When Scr and urine output were used as predictors, models that included clinical notes and concepts together with clinical variables performed on par with models that only used clinical variables. When excluding SCr and urine output, predictive performance improved by combining multiple modalities. The models that used only clinical variables were externally validated on the eICU dataset and transported fairly to the new population (AUROC 0.68-0.77). Our in-depth…
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Only 5 studies reported calibration metrics.
“In addition, only 5 studies reported calibration metrics, which are crucial for assessing model reliability [38,63,113,120,121].”
From Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review (2026) · cites this paper as [121]
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- Authors
- van Slobbe, R., Herrmannova, D., Boeke, D. J., Lima-Walton, E. S., Abu-Hanna, A., & Vagliano, I.
- Affiliations
- Elsevier BV (Netherlands) (NL)
- Amsterdam Public Health (NL)
- Corresponding author
- Vagliano, I.
- Venue
- International Journal of Medical Informatics
- Publisher
- Elsevier BV
- Published
- 2025-04
- Volume
- 196
- Pages
- 105815
- ISSN
- 1386-5056 (ISSN-L)
- OpenAlex
- W4407134183
- Semantic Scholar
- 1efbb7fc25157a08cd42cb728aeb53815b8aa197
- Cited by
- 7
- References
- 54
- Citations by year
- 2026: 5 · 2025: 2
- FWCI
- 2.28
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Medical Informatics (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (submittedVersion, cc-by)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (628011026, 2021.024)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Nephrology › Acute Kidney Injury Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- acute kidney injury, intensive care unit, multimodal prediction, clinical notes, clinical concepts, knowledge base, serum creatinine, urine output, convolutional neural networks, MIMIC database, eICU, external validation, model portability, early outcome prediction
- MeSH terms
- Convolutional Neural Networks, Creatinine, Critical Care, Humans, Intensive Care Units, Acute Kidney Injury
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