An Adaptive Multi-Indicator Contrastive Predictive Coding Framework for Patient Representation Learning

Effective patient representation learning from Electronic Health Records (EHR) is essential for improving disease prediction models, yet it faces critical challenges such as the scarcity of labeled data and the difficulty of capturing complex temporal and multi-indicator relationships. To address these limitations, we propose the Adaptive Multi-Indicator Contrastive Predictive Coding (AMCPC) framework, a self-supervised learning approach designed for EHR data. AMCPC incorporates two key innovations: first, it employs an adaptive optimal window size selection algorithm to segment patient visit sequences into temporal subwindows, which enables the model to focus on localized, context-specific health patterns; second, it extends Contrastive Predictive Coding (CPC) with a multi-indicator approach, leveraging a 2D convolutional neural network (CNN) to capture global correlations among diverse medical indicators within each subwindow. Through extensive experiments on real-world clinical datasets, we demonstrate that AMCPC outperforms both fully-supervised and existing self-supervised methods in disease prediction tasks, particularly when trained on limited labeled data. Our results establish AMCPC as an effective framework for leveraging unlabeled EHR data for self-supervised pretraining, which can then be fine-tuned with a small amount of labeled data to significantly enhance downstream prediction performance, reducing reliance on large-scale labeled datasets.