This research assesses data provenance in widely used health datasets, revealing flaws that could undermine clinical prediction models and patient care.

Some AI models designed to predict stroke and diabetes risk may be based on datasets whose origins cannot be verified, ...

The study published in the journal BMC Medicine was led by researchers at the Queensland University of Technology and the ...

AI tools are proliferating across pulmonary medicine and critical care, with promising early results in diagnostics and ...

Recent technological advances have opened valuable possibilities for supporting people with motor impairments or who are ...

In a suburb of east London, an illegal tip has been catching alight for years. Although residents and experts suspect it is ...

A researcher at LSU Health Shreveport has been awarded a grant of just over $200,000 to study how to predict and prevent heat illness in student-athletes.

Timely and accurate prediction of poststroke motor outcome is important for efficient rehabilitation planning and resource allocation. Existing bedside models for predicting upper-limb outcome after ...

Abstract: This study introduces a robust machine learning framework that aims at enhancing stroke risk prediction and supporting global health objectives to reduce stroke-related morbidity and ...

Timely recognition of oral anticoagulant use is critical in acute stroke but is often hampered by impaired consciousness and unavailable medication history. We investigated whether routinely available ...

Copyright: © 2025 The Author(s). Published by Elsevier Ltd. Machine learning for health data science, fuelled by proliferation of data and reduced computational ...