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 ...

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 ...

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 ...

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, ...

We identified patients with cancer older than 75 years from the National Emergency Department Sample between 2016 and 2018. We constructed a high-dimensional predictive model called Cancer Frailty ...

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 ...

Everyone wants predictive algorithms to be accurate, but there are different ways to define accuracy. Is it better to have an algorithm that's rarely perfect, but also rarely off by a mile? Or to have ...

The history of the social sciences has included a succession of advances in the ability to make observations and carefully test hypotheses. The compilation of massive data sets, for example, and the ...