TADA: Temporal-aware Adversarial Domain Adaptation for patient outcomes forecasting

Abstract

Patient outcomes forecasting (POF) has been shown to be an effective diagnostic assistant that can be used to predict disease progression and patient status in advance. In practice, the variation in patient progress and physical symptoms poses significant challenges for POF model training and testing. These differences result in POF models trained on one set of patients (source domain) not being efficiently self-adapted to the disease prediction task of another set of patients (target domain). Domain adaptive approaches can balance the overall distribution differences, named long-term divergence between patient training data and test data. However, the complex time series dependence and multivariate distribution properties of patient clinical data lead to the inability of existing domain adaptation methods to guarantee consistent distribution of patient clinical data at a finer granularity, named short-term divergence, e.g., clinical data distribution at different time intervals. In this paper, we propose a novel POF approach (TADA) based on clinical time series data using Temporal-aware Adversarial Domain Adaptation. In TADA, the traditional binary domain labels are generalized to act as the supervision signals to adversarially bridge the domain gap considering both long-term and short-term clinical information. TADA not only preserves the long-term representation of the clinical data but also captures the short-term data distribution at each period. The experimental results, based on both the publicly available and private clinical time series datasets, demonstrate that TADA can provide more reliable forecasting than state-of-the-art approaches.