Machine Learning Leveraged to Continuously Enrich Database
and Enhance Algorithms
The innovative sophisticated methodologies used in developing the Ahead® classification systems include those that have their foundation in big data and machine learning from genomics, neuroimaging and proteomics.
- At the core of these is information derived from brain electrical activity allowing the objective quantification of biomarkers of traumatic brain injury.
- In order to build a robust, validated classification algorithm, careful attention is paid to data reduction prior to selecting candidate features for algorithm development.
- The binary discriminant classification functions were derived using methods including genetic algorithms, a form of evolutionary algorithms.
- Evolutionary algorithms perform a stochastic search and evaluate a series of candidate solutions, where each new candidate is informed by high-performing predecessors, similar to genetic evolution.
- The final classifier functions consist of weighted combinations of selected linear and nonlinear features that reflect brain electrical activity which mathematically describes traumatic structural brain injury as distinguished from normal or concussed brain activity.
- Details on the development and performance of the algorithms have been published in the peer- reviewed literature.