To date, symptom documentation has mostly relied on clinical notes in electronic health
records or patient-reported outcomes using disease-specific symptom inventories. To …

Background In health care, diagnosis codes in claims data and electronic health records
(EHRs) play an important role in data-driven decision making. Any analysis that uses a …

Introduction Machine learning (ML) has great potential for using health data to predict
clinical outcomes in individual patients. Missing data are a common challenge in training ML …

Artificial intelligence (AI) applications have become widely popular across the healthcare
ecosystem. Colon capsule endoscopy (CCE) was adopted in the NHS England pilot project …

People face difficulties in obtaining various care services, which leads to a severe delay in
health care treatment, especially in light of the events that accompanied the spread of the …

The rapid evolution of information systems and the surge in data generation and
dissemination have fostered the emergence of Electronic Health Records (EHRs) …

Biomedical article extraction is the preliminary step for every biomedical application. These
applications are helpful in finding the gene, disease, chemical, drugs, protein entities …

Objective: To determine the effects of using unstructured clinical text in machine learning
(ML) for prediction, early detection, and identification of sepsis. Materials and methods …

Background: The constant upgrades and improvements in Electronic Health Record systems
may influence the perceptions and views of healthcare workers. The dynamicity may affect …

This study investigated information literacy, social media use, and knowledge sharing
practices among Undergraduates at the Federal University Oye-Ekiti, Nigeria. The study …