Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks

Abstract

Objective: Development 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 methods: This 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.

Results: When 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).

Conclusions: Self-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.

Keywords: electronic health records; foundation model; machine learning; model robustness; self-supervised learning; transfer learning.

Figure 1.

To prevent data leakage, patients in the validation and test sets of the adult inpatient cohort were excluded from pretraining in adult inpatients. Patients in the validation and test sets of the pediatric inpatient cohort were excluded from pretraining in pediatric inpatients. Patients in all validation and test sets were excluded from pretraining in combined adult and pediatric inpatients. For the task-specific exclusion counts, Hospital Mortality and Long LOS are listed as n/a as the relevant exclusions were performed at the “Exclude admission if patient death or discharge occurred between admission and prediction time” step. Abbreviation: LOS: length of stay.

Figure 2.

Figure shows task-specific AUROC for Adults→Adults (pretraining in adult inpatients with task-specific training in adult inpatients) and PedsBaseline (logistic regression trained in pediatric inpatients), both evaluated in pediatric inpatients (target=pediatric inpatients). Abbreviations: AUROC, area under the receiver operating characteristic curve; LOS, length of stay.