A machine learning model improves prediction of type 1 diabetes risk compared with a conventional genetic risk model, particularly in people without high-risk human leukocyte antigen haplotypes.
Cleveland Clinic and IBM researchers are using quantum computing to tackle one of the most challenging problems in immuno-oncology: predicting which tumor mutations will trigger an immune response.
BACKGROUND: Screening for atrial fibrillation (AF) on the basis of AF risk may be more effective. We aimed to develop, ...
Cost-Effectiveness of Maintaining Higher Stem-Cell Collection Thresholds in the Chimeric Antigen Receptor T-Cell Era for Multiple Myeloma Predicting severe adverse events (SAEs) in oncology is ...
A machine-learning model developed by Weill Cornell Medicine investigators may provide clinicians with an early warning of a complication that can occur late in pregnancy. Preeclampsia is a sudden ...
Association of the 70-gene assay and homologous recombination deficiency in patients with high risk 2 breast cancers. Model performance by outcome and number of variables. a Concordance index; scores ...
A new study shows that machine-learning models can accurately predict daily crop transpiration using direct plant measurements and environmental data. By training models on seven years of ...
Predicting earthquakes has long been an unattainable fantasy. Factors like odd animal behaviors that have historically been thought to forebode earthquakes are not supported by empirical evidence. As ...
A recent study published in the Journal of Hepatology demonstrated that a machine learning–based pan-elastography model can ...
Sports prediction did not begin with artificial intelligence, data dashboards or machine learning models. Long before modern ...
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