[2603.20921] Discriminative Representation Learning for Clinical Prediction

Computer Science > Machine Learning arXiv:2603.20921 (cs) [Submitted on 21 Mar 2026] Title:Discriminative Representation Learning for Clinical Prediction Authors:Yang Zhang, Li Fan, Samuel Lawrence, Shi Li View a PDF of the paper titled Discriminative Representation Learning for Clinical Prediction, by Yang Zhang and Li Fan and Samuel Lawrence and Shi Li View PDF HTML (experimental) Abstract:Foundation models in healthcare have largely adopted self supervised pretraining objectives inherited from natural language processing and computer vision, emphasizing reconstruction and large scale representation learning prior to downstream adaptation. We revisit this paradigm in outcome centric clinical prediction settings and argue that, when high quality supervision is available, direct outcome alignment may provide a stronger inductive bias than generative pretraining. We propose a supervised deep learning framework that explicitly shapes representation geometry by maximizing inter class separation relative to within class variance, thereby concentrating model capacity along clinically meaningful axes. Across multiple longitudinal electronic health record tasks, including mortality and readmission prediction, our approach consistently outperforms masked, autoregressive, and contrastive pretraining baselines under matched model capacity. The proposed method improves discrimination, calibration, and sample efficiency, while simplifying the training pipeline to a single stage optimi...