Series 1: Improving Outcomes Through Enhanced Data Analytics and Artificial Intelligence
Automated, complex, and sophisticated knowledge extraction in the form of artificial intelligence (AI) is maturing rapidly for application to every sector including the traditionally recalcitrant healthcare and life sciences (Pharma and Biotech). With ever-increasing capabilities in big data generation and utilization, adopting this digital revolution is critical for organizations to be successful.
This four-part feature discusses: (i) testing analytics workflows with the right data and metrics, (ii) what happens to your favorite analytics’ accuracy and ability to derive novel insights when it comes to real-world big data and unknown error properties, (iii) the reality of the knowledge repositories upon which our AI engines rely, and (iv) if machine learning is the cure for limitation of statistics and enterprise productivity.
Part 3: Coming soon.
Part 4: Coming soon.
The results and insights in these white papers are applicable to most multiple input --> output/outcome scenario as they are data property-dependent and are not restricted to any data source (i.e., data-source agnostic). The two technologies used here represent two extreme cases (as shown pictorially using the specific technologies used here):
(i) where multiple inputs are discrete measures to predict an output,
(ii) no apparent pattern exists in the multiple inputs.