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Explainable machine learning to identify risk factors for unplanned hospital readmissions in Nova Scotian hospitals.

Computers in biology and medicine
OBJECTIVE: A report from the Canadian Institute for Health Information found unplanned hospital readmissions (UHR) common, costly, and potentially avoidable, estimating a $1.8 billion cost to the Canadian healthcare system associated with inpatient r...

Multicenter Development and Prospective Validation of eCARTv5: A Gradient-Boosted Machine-Learning Early Warning Score.

Critical care explorations
BACKGROUND: Early detection of clinical deterioration using machine-learning early warning scores may improve outcomes. However, most implemented scores were developed using logistic regression, only underwent retrospective validation, and were not t...

Demographic bias of expert-level vision-language foundation models in medical imaging.

Science advances
Advances in artificial intelligence (AI) have achieved expert-level performance in medical imaging applications. Notably, self-supervised vision-language foundation models can detect a broad spectrum of pathologies without relying on explicit trainin...

Predicting Risk for Patent Ductus Arteriosus in the Neonate: A Machine Learning Analysis.

Medicina (Kaunas, Lithuania)
: Patent ductus arteriosus (PDA) is common in newborns, being associated with high morbidity and mortality. While maternal and neonatal conditions are known contributors, few studies use advanced machine learning (ML) as predictive factors. This stud...

Physiological Sensor Modality Sensitivity Test for Pain Intensity Classification in Quantitative Sensory Testing.

Sensors (Basel, Switzerland)
Chronic pain is prevalent and disproportionately impacts adults with a lower quality of life. Although subjective self-reporting is the "gold standard" for pain assessment, tools are needed to objectively monitor and account for inter-individual diff...

Deep learning based quantitative cervical vertebral maturation analysis.

Head & face medicine
OBJECTIVES: This study aimed to enhance clinical diagnostics for quantitative cervical vertebral maturation (QCVM) staging with precise landmark localization. Existing methods are often subjective and time-consuming, while deep learning alternatives ...

Predicting rheumatoid arthritis progression from seronegative undifferentiated arthritis using machine learning: a deep learning model trained on the KURAMA cohort and externally validated with the ANSWER cohort.

Arthritis research & therapy
BACKGROUND: Undifferentiated arthritis (UA) often develops into rheumatoid arthritis (RA), but predicting disease progression from seronegative UA remains challenging because seronegative RA often does not meet the classification criteria. This study...

Interpretable machine learning method to predict the risk of pre-diabetes using a national-wide cross-sectional data: evidence from CHNS.

BMC public health
OBJECTIVE: The incidence of Type 2 Diabetes Mellitus (T2DM) continues to rise steadily, significantly impacting human health. Early prediction of pre-diabetic risks has emerged as a crucial public health concern in recent years. Machine learning meth...

Perceived artificial intelligence readiness in medical and health sciences education: a survey study of students in Saudi Arabia.

BMC medical education
BACKGROUND: As artificial intelligence (AI) becomes increasingly integral to healthcare, preparing medical and health sciences students to engage with AI technologies is critical.