Liyue Fan

Data synthesis can address important data availability challenges in biomedical informatics. Quantitative evaluation of generative models may help understand their applications to synthesizing biomedical data. This poster paper examines state-of-the-art...

Effective disease surveillance systems require large-scale epidemiological data to improve health outcomes and quality of care for the general population. As data may be limited within a single site, multi-site...

Deep neural networks have been increasingly integrated in healthcare applications to enable accurate predicative analyses. Sharing trained deep models not only facilitates knowledge integration in collaborative research efforts but also...

Measuring spatial accessibility to healthcare resources and facilities has long been an important problem in public health. For example, during disease outbreaks, sharing spatial accessibility data such as individual travel...

Sharing health data is vital in advancing medical research and transforming knowledge into clinical practice. Meanwhile, protecting the privacy of data contributors is of paramount importance. To that end, several...

Sharing time-to-event data is beneficial for enabling collaborative research efforts (e.g., survival studies), facilitating the design of effective interventions, and advancing patient care (e.g., early diagnosis). Despite numerous privacy solutions...

Objective: Emerging technologies (eg, wearable devices) have made it possible to collect data directly from individuals (eg, time-series), providing new insights on the health and well-being of individual patients. Broadening...

Temporal medical data are increasingly integrated into the development of data-driven methods to deliver better healthcare. Searching such data for patterns can improve the detection of disease cases and facilitate...