Clinically-applicable prediction of hospital stay and patient similarity retrieval in paediatric cardiology using machine learning
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Paediatric cardiology presents challenges due to the rarity and complexity of conditions like congenital heart disease. Using retrospective electronic healthcare records from 1,522 Great Ormond Street Hospital cases, we benchmark machine learning models to predict length of stay and retrieve similar patient cases. BioClinical-BERT is used for embedding-based retrieval, while a Random Forest model achieves the best length of stay prediction accuracy (0.88 ± 0.02), outperforming clinicians. The Random Forest model shows mean precision and sensitivity of 0.77 ± 0.03 and 0.76 ± 0.04 during NHS silent deployment (1,052 admissions). K-means identifies three clinically distinct subgroups. Cosine similarity retrieval reveals diagnosis-driven top matches, while complications dominate broader sets. In a 25-case intensive care unit pilot, clinician-rated utility improves from 4.23 ± 2.42 to 4.41 ± 2.16 (scale 0-10). Our models surpass clinician performance in length of stay prediction and show promise for case retrieval, supporting data-driven decision-making in paediatric cardiology and beyond.
Date Issued
2026-05-13
Date Acceptance
2026-04-28
Citation
Nature Communications, 2026, 17
ISSN
2041-1723
Publisher
Nature Portfolio
Journal / Book Title
Nature Communications
Volume
17
Copyright Statement
© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/42129161
PII: 10.1038/s41467-026-73021-3
Publication Status
Published
Coverage Spatial
England
Article Number
6370
Date Publish Online
2026-05-13