Uniting ML Community for
Health Time-Series
Time-series data underpin modern healthcare, spanning electronic health records, physiological waveforms, wearables, and population trends, yet their unique characteristics—including uncertain ground truth, quasi-periodic physiological motifs, and non-semantic timepoints—demand specialized machine learning approaches.
While recent advances in foundation models, multimodal learning, and generative methods show promise, significant challenges remain in causality, interpretability, and deployment.
The TS4H workshop unites researchers across health time-series domains—from wearables to clinical systems—to address shared challenges through cross-domain discussion, diverse industry and academic perspectives, and community engagement via posters, talks, panels, and mentorship activities.
