Multimodal machine learning (MML) is a tempting multidisciplinary research area where
heterogeneous data from multiple modalities and machine learning (ML) are combined to …

Biomedical-named entity recognition (bNER) is critical in biomedical informatics. It identifies
biomedical entities with special meanings, such as people, places, and organizations, as …

Pancreatic cancer is associated with higher mortality rates due to insufficient diagnosis
techniques, often diagnosed at an advanced stage when effective treatment is no longer …

Monitoring the health status of patients and predicting mortality in advance is vital for
providing patients with timely care and treatment. Massive medical signs in Electronic Health …

Type 2 diabetes (T2D) is a worldwide chronic disease that is difficult to cure and causes a
heavy social burden. Early prediction of T2D can effectively identify high-risk populations …

Background While many falls are preventable, they remain a leading cause of injury and
death in older adults. Primary care clinics largely rely on screening questionnaires to identify …

Background Clinical machine learning (ML) technologies can sometimes be biased and
their use could exacerbate health disparities. The extent to which bias is present, the groups …

The irregularity of the time interval between observations in and across the stream is a key
factor that leads to a drop in performance when classical machine learning or deep learning …

The high-dimensionality, complexity, and irregularity of electronic health records (EHR) data
create significant challenges for both simplified and comprehensive health assessments …

The application of artificial intelligence methods to electronic patient records paves the way
for large-scale analysis of multimodal data. Such population-wide data describing deep …