By illuminating common pitfalls in clinical studies, we aim to pave the way to consistent strategic investment in the healthcare sector
Deep neural nets, AI, deep learning, next generation machine learning– they are in the news, they are in the proposals you’re evaluating. Do you need them?
Failure of a new tool may be due to the use of predictors that don’t add information to that already provided by standard clinical variables.
Failure of a new tool may be due to a reliance on variables that don’t directly cause the outcome being predicted. The predictor variables and the outcome may both be influenced by a third variable, called a “confounding variable”
Failure of a new tool is often due to lack of diversity in the initial patient set and from analytical methods that are blind to this problem
HelixHarbor Analytics
info@HelixHarborAnalytics.com
+1 (256) 932-6960
Huntsville, Alabama & Silicon Valley, California
USA
