It is of great interest to develop and introduce new techniques to automatically and efficiently
analyze the enormous amount of data generated in today's hospitals, using state-of-the-art …

Electronic medical records contain multi-format electronic medical data that consist of an
abundance of medical knowledge. Facing with patient's symptoms, experienced caregivers …

With the growth of data and intelligent technologies, the healthcare sector opened numerous
technology that enabled services for patients, clinicians, and researchers. One major hurdle …

With the effective adoption of EHRs in clinical care, an increasing number of researchers
contribute to meaningful use of EHRs for advancing in performance of patient similarity …

Medical diagnosis is the process of making a prediction of the disease a patient is likely to
have, given a set of symptoms and observations. This requires extensive expert knowledge …

In recent years, prediction of 30-day hospital readmission risk received increased interest in
the area of Healthcare Predictive Analytics because of high human and financial impact …

In recent years, knowledge graphs (KGs) have been considered pyramids of interconnected
data enriched with semantics for complex decision-making. The potential of KGs and the …

The incorporation of data analytics in the healthcare industry has made significant progress,
driven by the demand for efficient and effective big data analytics solutions. Knowledge …

There has been a steady growth in machine learning research in healthcare, however,
progress is difficult to measure because of the use of different cohorts, task definitions and …

Representation learning methods that transform encoded data (eg, diagnosis and drug
codes) into continuous vector spaces (ie, vector embeddings) are critical for the application …