In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

Cardiovascular disease (CVD) is the leading cause of death worldwide and is a global
health issue. Traditionally, statistical models are used commonly in the risk prediction and …

Background: Learning a model without accessing raw data has been an intriguing idea to
security and machine learning researchers for years. In an ideal setting, we want to encrypt …

Background Security concerns have been raised since big data became a prominent tool in
data analysis. For instance, many machine learning algorithms aim to generate prediction …

Objective: Predictive models that generate individualized estimates for medically relevant
outcomes are playing increasing roles in clinical care and translational research. However …

Objective The classification of complex or rare patterns in clinical and genomic data requires
the availability of a large, labeled patient set. While methods that operate on large …

This thesis examines how discernibility-based methods can be equipped to posses several
qualities that are needed for analyzing tabular medical data, and how these models can be …

There is a growing body of research focusing on automatic detection of ischemia and
myocardial infarction (MI) using computer algorithms. In clinical settings, ischemia and MI …

Background To accelerate healthcare/genomic medicine research and facilitate quality
improvement, researchers have started cross-institutional collaborations to use artificial …

Objective Decentralized privacy-preserving predictive modeling enables multiple institutions
to learn a more generalizable model on healthcare or genomic data by sharing the partially …