Predicting Partially Observed Long-Term Outcomes with Adversarial Positive-Unlabeled Domain Adaptation

Predicting long-term clinical outcomes often requires large-scale training data with sufficiently long follow-up. However, in electronic health records (EHR) data, long-term labels may not be available for contemporary patient cohorts. Given the dynamic nature of clinical practice, models that rely on historical training data may not perform optimally. In this work, we frame the problem as a positive-unlabeled domain adaptation task, where we seek to adapt from a fully labeled source domain (e.g., historical data) to a partially labeled target domain (e.g., contemporary data). We propose an adversarial framework that includes three core components: (1) Overall Alignment, to match feature distributions between source and target domains; (2) Partial Alignment, to map source negatives to unlabeled target samples; and (3) Conditional Alignment, to address conditional shift using available positive labels in the target domain. We evaluate our method on a benchmark digit classification task (SVHN-MNIST), and two real-world EHR applications: prediction of one-year mortality post COVID-19, and long-term prediction of neurodevelopmental conditions (NDC) in children. In all settings, our approach consistently outperforms baseline models and, in most cases, achieves performance close to an oracle model trained with fully observed labels.