Electronic health records contain large amounts of longitudinal data that are valuable for
biomedical informatics research. The application of machine learning is a promising …

Objective Temporal electronic health records (EHRs) contain a wealth of information for
secondary uses, such as clinical events prediction and chronic disease management …

Prediction of medical events, such as clinical procedures, is essential for preventing
disease, understanding disease mechanism, and increasing patient quality of care …

Electronic health records (EHRs) contain important temporal information about the
progression of disease and treatment outcomes. This paper proposes a transitive …

Objective Developing machine learning and data mining algorithms for building temporal
models of clinical time series is important for understanding of the patient condition, the …

Large-scale clinical databases provide a detailed perspective on patient phenotype in
disease and the characteristics of health care processes. Important information is often …

Biomedical data, in particular electronic medical records data, include a large number of
variables sampled in irregular fashion, often including both time point and time intervals …

The rapid growth in the development of healthcare information systems has led to an
increased interest in utilizing the patient Electronic Health Records (EHR) for assisting …

In this work, we propose a novel autoregressive event time-series model that can predict
future occurrences of multivariate clinical events. Our model represents multivariate event …

The increasing availability of complex temporal clinical records collected today has
prompted the development of new methods that extend classical machine learning and data …