Objective Although the role of artificial intelligence (AI) in medicine is increasingly studied,
most patients do not benefit because the majority of AI models remain in the testing and …

Abstract Information systems such as Electronic Health Record (EHR) systems are
susceptible to data quality (DQ) issues. Given the growing importance of EHR data, there is …

Early and accurate diagnosis is crucial to prevent disease development and define
therapeutic strategies. Due to predominantly unspecific symptoms, diagnosis of autoimmune …

Even though data preparation constitutes a large proportion of the total effort involved in
electronic health record (EHR) based secondary analysis, guidelines for EHR data …

Data preprocessing is a fundamental stage in deep learning modeling and serves as the
cornerstone of reliable data analytics. These deep learning models require significant …

Objective: Storage of physiological waveform data for retrospective analysis presents
significant challenges. Resultant data can be very large, and therefore becomes expensive …

The rapid emergence of antibiotic-resistant bacteria is recognized as a global healthcare
crisis, undermining the efficacy of life-saving antibiotics. This crisis is driven by the improper …

Big data analytics research using heterogeneous electronic health record (EHR) data
requires accurate identification of disease phenotype cases and controls. Overreliance on …

Background Electronic health record (EHR) data is commonly used for secondary purposes
such as research and clinical decision support. However, reuse of EHR data presents …

Machine learning techniques are being used in cancer research for more than a decade.
Nowadays, Machine Learning Algorithms (ML a) can contribute significantly to the area of …