Subject
Machine learning, neural networks, language models, and computer vision.
A study of 139 Beijing adults found strong internal performance when machine-learning models separated phlegm-dampness and balanced constitutions, but larger validation is still needed.
Frontiers in endocrinology4 min read
A machine-learning model identified concurrent fear of movement in older patients after coronary procedures, with strong results in a time-separated test group but important limits on wider use.
Frontiers in medicine4 min read
A model built from six clinical measures performed better than three comparison methods in an internal test of 518 children with allergic rhinitis. The study found associations involving IgE, Vitamin D, BMI, sleep and screen time, but it did not show that any of these factors causes ADHD or that changing them would alter symptoms.
Frontiers in public health5 min read
In a retrospective study, LightGBM ranked in-hospital mortality risk in a held-out ICU cohort and an external database, but only 30.3% of external high-risk predictions were followed by death.
Frontiers in neurology5 min read
Among 768 young and middle-aged adults receiving maintenance hemodialysis, 40.23% were in paid work at survey time, and a random-forest model performed well in an independent group of 203 patients.
Frontiers in public health4 min read
Researchers compared machine-learning and Cox models for predicting recorded fasting-glucose-defined prediabetes in adults whose baseline glucose was normal. The selected model performed moderately in internal and hospital-based external testing, but the study did not test whether using it improves care.
Frontiers in endocrinology4 min read
Camera-based AI scoring closely matched consensus video review for total BESS scores and produced smaller discrepancies than real-time on-site manual scoring in 80 participants.
Sports Medicine - Open4 min read
The model was strongest on rhythm abnormalities and weaker on conduction and morphological conditions, with results varying across diagnoses and datasets.
Health information science and systems4 min read
Researchers found immune and tissue-related gene signals in separate datasets on disc degeneration and osteoporosis, but say the findings need larger, clinically matched human studies.
Frontiers in immunology5 min read
Researchers built a mobile system that reminds people to take medicines, collects short intake videos and uses artificial intelligence to classify what is happening. The model performed well on held-out test data, but the clinical pilot was small, short and uncontrolled.
Revista da Associacao Medica Brasileira (1992)5 min read
A conceptual framework proposes hybrid AI support for medication optimization under clinician oversight, but the paper reports no validation or real-world performance evidence.
International journal of clinical pharmacy3 min read
A public EMG-IMU dataset produced near-99% benchmark accuracy for attention-enhanced models, while the test design limits what the scores say about unseen users and real-world use.
Scientific reports4 min read
A machine-learning model identified liver fibrosis more accurately than standard scoring tools in a validation group of people previously infected with Schistosoma japonicum. The result is promising, but the study was cross-sectional and drew on one province in China, so it does not yet show that the model improves care or works elsewhere.
Infectious diseases of poverty4 min read
The models tracked measured exon inclusion well in some assays, but interpretable surrogates exposed sequence biases and missed structure-linked changes in selected variants.
Genome biology4 min read
SPALT combines local sensor patterns with a pruned linear model tree, and its overall accuracy advantage came with a smaller model rather than a faster one.
Machine Learning, Volume 114, article number 231 (2025)4 min read