Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and data preprocessing. If you''ve ever built a predictive model, worked on a ...

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 ...

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 ...

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 ...

To prevent algorithmic bias, the authors call for multivariable modeling frameworks that jointly incorporate biological sex, genetic ancestry, and gender-related life-course exposures.

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, ...

A new algorithm aims to assess the likelihood of defendants being treated unfairly in court. The tool considers details that ought to be immaterial to the ruling — such as the judge’s and defendant’s ...

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 ...