Papers for
health data platform developers
Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.
Extending data privacy vocabulary to cover new German health law
Extending the W3C Data Privacy Vocabulary to Cover the German Health Data Use Act - Lessons Learned
Abstract: Finding, sharing, and reusing data requires common interoperable vocabularies to adequately describe machine-actionable data policies. Semantic Web standards are a proven way to express such information describing data access and reuse. Narrative descriptions get a stable and agreed meaning by formal definitions of terms that have a globally unique identifier. The Data Privacy Vocabulary (DPV) represents a community standard for privacy, data protection, and related topics. DPV uses modular namespaces to define jurisdictions, such as "EU", and to represent concepts from laws within that jurisdiction, such as the General Data Protection Regulation (GDPR) and the European Health Data Space (EHDS). In this work, we present the creation of a DPV extension representing the German Health Data Use Act (Gesundheitsdatennutzungsgesetz (GDNG)) that has been set into force in March 2024. The GDNG regulates sharing and using health data for secondary use, such as research purposes and policymaking. It builds the German legal foundation for the future EHDS as the European initiative and regulation providing rules for sharing of health data within Europe. The parallel development of the EHDS and GDNG extensions resulted in many shared concepts, which contributed to an overarching extension: the DPV sector health extension. It sets the foundation of aligned privacy terms for future EHDS member states looking to build a national vocabulary aligned with the EHDS. All extensions were released as draft extensions with the latest DPV version 2.3, released 25th of February 2026.
Wearable ppg representations improve health monitoring with multi-scope alignment
HiPPT: Physiology-Grounded Wearable PPG Representation Learning via Multi-Scope Text Alignment
Abstract: Reusable wearable PPG representations should capture local pulse morphology and broader physiological regularities. Existing pretraining objectives, however, usually apply supervision at the recording or pair level, overlooking that different concepts become reliable over different scopes of waveform evidence. We propose HiPPT, a physiology-grounded framework that matches each supervision source to its appropriate evidence scope. We construct a PPG-Text corpus of over 4 million wrist-worn recordings from 202K users, pairing recordings with waveform-derived morphology and metadata-derived context descriptions. HiPPT combines record-level morphology alignment, which grounds individual recordings in pulse characteristics, with cross-recording context alignment, which aggregates recordings sharing the same metadata tuple to capture context-consistent regularities. Waveform-guided text modeling further recovers masked contextual concepts from PPG patch representations, providing fine-grained waveform-text supervision. Across six downstream tasks spanning blood pressure, vascular stiffness, physiological attributes, and cross-dataset heart-rate estimation, HiPPT achieves the best result on the primary linear-probing metrics. The aligned space also supports zero-shot profiling of age group, sex, and BMI category, individually and jointly, with performance improving as more recordings are aggregated. These results show that evidence-scope matching yields transferable and semantically accessible wearable PPG representations. Code and pretrained weights will be released.