Chronic disease progression is emerging as an important area of investment for healthcare
providers. As the quantity and richness of available clinical data continue to increase along …

Heart failure management is challenging due to the complex and heterogenous nature of its
pathophysiology which makes the conventional treatments based on the “one size fits all” …

Deep learning sequential models are increasingly being used to predict patients' health
outcomes by analyzing their medical histories. In this paper, we investigate the design …

Predictive modeling with electronic health record (EHR) data is anticipated to drive
personalized medicine and improve healthcare quality. Constructing predictive statistical …

We propose to apply deep Graph Convolutional Network (GCN) for the analysis and
prediction of patient health comorbidity from sparse health records. Patient health data are …

Time irregularity is common in medical visit record data. For disease progress prediction,
irregular time intervals occur in two places. One is in the input historical data for prediction …

Effective modeling of electronic health records (EHR) is rapidly becoming an important topic
in both academia and industry. A recent study showed that utilizing the graphical structure …

In-hospital clinical deterioration could lead to unfavorable adverse events, such as mortality,
cardiac arrest, or unplanned admission to the intensive care unit. Early detection of …

Recently, there is great interest to investigate the application of deep learning models for the
prediction of clinical events using electronic health records (EHR) data. In EHR data, a …

With the digitization of health records over the last two decades there is a large amount of
health records data collected electronically. This data provides unprecedented research …