Trust the System: Auditing Privacy- preserving Medical Data Analysis in a Distributed Manner

Recent developments in the capability and availability of small internet of things devices has meant that networked medical devices, like networked implants and wearable monitors, have become more widespread. This data is invaluable for solving pressing global healthcare concerns, like eectively monitoring and treating heart patients. The...

Leveraging Related Datasets to Improve Model Performance on an Underrepresented Target Population

Training deep learning models for time-series prediction of a target population often requires a substantial amount of training data, which may not be readily available. This work addresses the challenge of leveraging multiple related sources of time series data in the same feature space to improve the prediction performance of a deep learning...

Security and privacy in medical data sharing through blockchain

The sharing of medical data is becoming ever more important. More and more health-related data is being generated everyday and as will be shown later, its primary as well as its secondary usage brings many benefits to the healthcare system. However, medical data systems are not fail proof and are often the target of cyber-attacks, compromising...

Managing medical data collection from multiple sources

Nowadays, health care institutions do not only store health care information generated at their own facility, but also information retrieved from other institutions. This distribution of data over different institutions raises problems of duplicate and conflicting data. Recently a new standard for exchanging medical data has been developed: FHIR...