ArticleCritical care (London, England)2026
Multi-source data integration through pooling and transfer learning improves generalizability and specialization of deep learning models for ICU mortality and length of stay prediction: a four-database external validation study.
Article in Critical care (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Machine learning-based prediction of prolonged length of stay in older patients with type 2 diabetes mellitus and cardiovascular disease.Frontiers in cardiovascular medicine · 2026Article
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6 authors.
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Abstract
backgroundMost Artificial Intelligence prognostic models in intensive care are trained and validated on a single source, limiting their reliability in external settings. This study evaluated generalizability and specialization for ICU mortality and remaining length of stay (RLoS) prediction across international databases, and explored multi-source strategies to mitigate the performance cost of external deployment.
methodsUsing the Temporal Pointwise Convolution (TPC) architecture, we conducted external validation across four harmonized BlendedICU databases (eICU-CRD, MIMIC-IV, AmsterdamUMCdb, HiRID), extracting nearly 20,000 unique patients from each. Four training configurations were compared: single-source training on AmsterdamUMC (N = 3,574) or MIMIC-IV (N = 10,915), data pooling, and transfer learning (both N = 14,489). Performance was assessed using AUROC and AUPRC for mortality, MAPE for RLoS, and a composite metric (Mcomposite).
resultsInternal validation consistently overestimated external performance, with MAPE increasing by up to 51.8% and AUROC dropping by up to 13.8% on external databases. Site-specific features such as drug exposure acted as "shortcuts" degrading portability. Data pooling emerged as the superior strategy, achieving the best generalization (Mcomposite improvement up to 8.0% over single-source models) while matching or outperforming transfer learning for specialization - particularly for RLoS (MAPE 84.49 vs. 89.24, non-overlapping confidence intervals).
conclusionSingle-source training compromises clinical applicability. Data pooling across harmonized international cohorts is the most effective strategy for both generalization and specialization, supporting a "Data-Centric AI" approach. We advocate for the democratization of local ICU datasets and open-source customizable models to empower independent clinical validation.
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