ArticleJournal of the American Medical Informatics Association : JAMIA2023
Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Secondary use of health records for prediction, detection, and treatment planning in the clinical decision support system: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Clinically-applicable prediction of hospital stay and patient similarity retrieval in paediatric cardiology using machine learning.Nature communications · 2026Article
- Transforming Pediatric Rare Disease Drug Development: Enhancing Clinical Trials and Regulatory Evidence With Virtual Patients.CPT: pharmacometrics & systems pharmacology · 2025Article
- A scoping review of self-supervised representation learning for clinical decision making using EHR categorical data.NPJ digital medicine · 2025Article
- Machine Learning in Pediatric Healthcare: Current Trends, Challenges, and Future Directions.Journal of clinical medicine · 2025Review
- Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Structured Data Analysis.Health data science · 2025Review
- A roadmap to implementing machine learning in healthcare: from concept to practice.Frontiers in digital health · 2025Article
- A multi-center study on the adaptability of a shared foundation model for electronic health records.NPJ digital medicine · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveDevelopment of electronic health records (EHR)-based machine learning models for pediatric inpatients is challenged by limited training data. Self-supervised learning using adult data may be a promising approach to creating robust pediatric prediction models. The primary objective was to determine whether a self-supervised model trained in adult inpatients was noninferior to logistic regression models trained in pediatric inpatients, for pediatric inpatient clinical prediction tasks. MATERIALS AND
methodsThis retrospective cohort study used EHR data and included patients with at least one admission to an inpatient unit. One admission per patient was randomly selected. Adult inpatients were 18 years or older while pediatric inpatients were more than 28 days and less than 18 years. Admissions were temporally split into training (January 1, 2008 to December 31, 2019), validation (January 1, 2020 to December 31, 2020), and test (January 1, 2021 to August 1, 2022) sets. Primary comparison was a self-supervised model trained in adult inpatients versus count-based logistic regression models trained in pediatric inpatients. Primary outcome was mean area-under-the-receiver-operating-characteristic-curve (AUROC) for 11 distinct clinical outcomes. Models were evaluated in pediatric inpatients.
resultsWhen evaluated in pediatric inpatients, mean AUROC of self-supervised model trained in adult inpatients (0.902) was noninferior to count-based logistic regression models trained in pediatric inpatients (0.868) (mean difference = 0.034, 95% CI=0.014-0.057; P < .001 for noninferiority and P = .006 for superiority).
conclusionsSelf-supervised learning in adult inpatients was noninferior to logistic regression models trained in pediatric inpatients. This finding suggests transferability of self-supervised models trained in adult patients to pediatric patients, without requiring costly model retraining.
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