With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …

Objective To describe and appraise the use of artificial intelligence (AI) techniques that can
cope with longitudinal data from electronic health records (EHRs) to predict health-related …

Multimodal electronic health record (EHR) data are widely used in clinical applications.
Conventional methods usually assume that each sample (patient) is associated with the …

Despite artificial intelligence (AI)'s significant growth, its “black box” nature creates
challenges in generating adequate trust. Thus, it is seldom utilized as a standalone unit in …

Predicting a patient's future health condition by analyzing their Electronic Health Records
(EHRs) is a trending subject in the intelligent medical field, which can help clinicians …

Predicting the patient's clinical outcome from the historical electronic medical records (EMR)
is a fundamental research problem in medical informatics. Most deep learning-based …

Forecasting influenza-like illness (ILI) is of prime importance to epidemiologists and health-
care providers. Early prediction of epidemic outbreaks plays a pivotal role in disease …

Objective: With the increasing amount and growing variety of healthcare data, multimodal
machine learning supporting integrated modeling of structured and unstructured data is an …

Clinical predictive models often rely on patients' electronic health records (EHR), but
integrating medical knowledge to enhance predictions and decision-making is challenging …

Irregularly sampled multivariate time series are ubiquitous in various fields, particularly in
healthcare, and exhibit two key characteristics: intra-series irregularity and inter-series …