News-Medical.Net

Machine learning predicts who will decline faster in Alzheimer’s disease using routine clinic data

Researchers developed and validated ElasticNet machine learning models that predict 12-month MMSE and BADL outcomes in ...
clinicaladvisor.com

Machine Learning Shows Promise in Assessing Psoriasis Severity

Machine learning models may be able to more accurately predict psoriasis severity vs dermatologists using the Psoriasis Area and Severity Index and Body Surface Area measures. A systematic review ...
Kaleido Scope

Deep machine-learning speeds assessment of fruit fly heart aging and disease, a model for human disease

Researchers at the University of Alabama at Birmingham now show a way to significantly cut the time needed for that analysis while utilizing more of the heart region, using deep learning and ...
Nature

Facial Paralysis Assessment Using Machine Learning Techniques

Facial paralysis, a condition characterised by impaired facial muscle movement, presents significant challenges both diagnostically and therapeutically. Recent advances in machine learning have ...
Science Daily

Researchers use machine learning to improve cardiovascular risk assessment

Researchers used advanced machine learning to increase the accuracy of a national cardiovascular risk calculator while preserving its interpretability and original risk associations. Risk calculators ...

It’s time to rethink assessment for learning

For decades, formative assessment has been a silent engine for learning—powering insights about student progress and worker ...
ascopubs.org

Development and fairness assessment of machine learning models for predicting 30-day readmission after lung cancer surgery.

Implementation of an academic precision oncology service in a community setting. This is an ASCO Meeting Abstract from the 2025 ASCO Annual Meeting I. This abstract does not include a full text ...
BMJ

Machine learning approaches to injury risk prediction in sport: a scoping review with evidence synthesis

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...