Chronic Obstructive Pulmonary Disease (COPD) is the most common chronic respiratory disease in the world. Fluctuations in symptoms frequently occur in COPD and might require additional treatments. Automatic monitoring or diagnosis of these fluctuations may aid the management of people with COPD. Speech analysis is a possible remote non-invasive methods to achieve this. However, this analysis is disease and possibly language specific and the development of speech models requires high-quality and high-quantity data, which are often not available. In this study we introduce SPEAKtoCOPD: a dataset of Dutch speech collected with a flash mob study targeting people with a respiratory disease (specifically COPD). In this paper, we describe the flash mob methodology, our collected data, and perform an automatic quality check to assess whether the speech tasks have been performed correctly. We aim to publish our gathered data for researchers to improve speech analysis for respiratory health.

The acoustic features of speech exhibit variations across different respiratory conditions, highlighting the potential of voice analysis as a valuable tool for non-invasive monitoring systems. Early detection of exacerbations plays a critical role in the effective management of chronic respiratory diseases, such as chronic obstructive pulmonary disease (COPD) and asthma. This paper presents the utilization of fused acoustic features from multiple domains, integrated with a Locally-enhanced Feed-Forward Network (LeFF) Transformer model, to classify exacerbated and stable speech in COPD and asthma patients. The proposed methodology is evaluated on the TACTICAS dataset, demonstrating superior performance compared to current state-of-the-art approaches, underscoring its potential for exacerbations monitoring in COPD and asthma patients.

Blockchain technology promises enhanced data ownership, control, and interoperability in healthcare, yet security and privacy concerns continue to hinder its adoption. Existing surveys examine blockchain-based privacy challenges, but they lack a systematic analysis and maturity evaluation of privacy-preserving techniques tailored to healthcare data sharing. This paper presents a systematic review of blockchain-based privacy-preserving solutions, analyzing blockchain details, applied privacy methods, regulatory compliance, and maturity levels using Technology Readiness Levels (TRLs). Our findings reveal that authentication and authorization is the most explored stage, dominated by smart contracts and ciphertext-policy attribute-based encryption. Proxy re-encryption is frequently used for data transfer, while privacy-preserving search and verification remain underexplored. On/off-chain mechanisms are commonly applied to balance privacy and storage efficiency. TRL assessment shows that most solutions remain at the proof-of-concept stage (TRL3), with only limited progress to prototype validation (TRL4-TRL5), highlighting the gap between experimental designs and real-world deployment. To guide developers and researchers, we identify two primary patterns of blockchain integration and propose a framework for system design. We also compare methods across data-sharing stages, outlining their strengths and limitations to support informed selection. In conclusion, while research interest is growing, the field remains at an early stage of maturity. Addressing this gap requires stronger implementation capacity, access to clinical data, and robust regulatory alignment. We emphasize the importance of clinical validation and real-world testing to advance privacy-preserving blockchain solutions toward practical adoption in healthcare.

Easy access to data is one of the main avenues to accelerate scientific research. As a key element of scientific innovations, data sharing allows the reproduction of results and helps prevent data fabrication, falsification, and misuse. Although the research benefits from data reuse are widely acknowledged, the data collections existing today are still kept in silos. Indeed, monitoring what happens to data once they have been handed to a third party is currently not feasible within the current data sharing practices. We propose a blockchain-based system to trace data collections and potentially create a more trustworthy data sharing process. In this paper, we present the LUCE (License accoUntability and CompliancE) architecture as a decentralized blockchain-based platform supporting data sharing and reuse. LUCE is designed to provide full transparency on what happens to the data after they are shared with third parties. The contributions of this work consist of i) the design of a decentralized data sharing solution with accountability and compliance by design and ii) the inclusion of a dynamic consent model for personalized data sharing preferences and for enabling legal compliance mechanisms. We test the scalability of the platform in a real-time environment where a growing number of users access and reuse different datasets. Compared to existing data sharing solutions, LUCE provides transparency over data sharing practices, enables data reuse, and supports regulatory requirements. The experimentation shows that the platform can be scaled for a large number of users.