Abnormality prediction and forecasting of laboratory values from electrocardiogram signals using multimodal deep learning.

ABSTRACT: This study investigates the feasibility of using electrocardiogram (ECG) data combined with basic patient metadata to estimate and monitor prompt laboratory abnormalities. We use the MIMIC-IV dataset to train multimodal deep learning models on ECG waveforms, demographics, biometrics, and vital signs. Our model is a structured state space classifier with late fusion for metadata. We frame the task as individual binary classifications per abnormality and evaluate performance using AUROC. The models achieve strong performance, with AUROCs above 0.70 for 24 lab values in abnormality prediction and up to 24 in abnormality forecasting, across cardiac, renal, hematological, metabolic, immunological, and coagulation categories. NTproBNP (≥ 353 pg/mL) is best predicted (AUROC > 0.90). Other values

SUBMITTER: Alcaraz JML 

PROVIDER: S-EPMC12603147 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature