Reviewed by Margaret JamesFact checked by Jared EckerReviewed by Margaret JamesFact checked by Jared Ecker Predictive modeling uses known results to create, process, and validate a model to forecast ...

Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...

Geisinger and IBM this week announced this week that they've co-created a new predictive model to help clinicians flag sepsis risk using data from the integrated health system's electronic health ...

shinyOPTIK, a User-Friendly R Shiny Application for Visualizing Cancer Risk Factors and Mortality Across the University of Kansas Cancer Center Catchment Area We trained and validated two-phase ML ...

The predictive variables assessed were age at EGPA diagnosis, baseline eosinophil count, history of chronic sinusitis prior to diagnosis, and glucocorticoid-treated asthma at diagnosis.

Hospitals are looking to invest in new technologies and work-on innovations that will improve the care patients receive. To learn more about how hospitals are adopting new technologies such as ...

Two new advanced predictive algorithms use information about a person's health conditions and simple blood tests to accurately predict a patient's chances of having a currently undiagnosed cancer, ...

Get the latest federal technology news delivered to your inbox. The Department of Veterans Affairs is in the process of adding additional risk factors to its artificial intelligence-powered tool for ...

MyHomeQuote introduced Performance Prediction Algorithm, technology designed to move campaigns from reactive optimization to predictive performance management.