The core of ARPS™ is neural network based analytics. Neural networks are essentially a computing paradigm that mimics the ability of the brain to recognize patterns and relationships within complex data sets. They are an advance over traditional computing techniques in that they discover relationships between large numbers of variables within massive data sets.

Allostatix's science and medical staff have combined longitudinal health data from a large number of national and international research studies with blood and biometric measurements to design a unique and proprietary system that predicts future health and identifies people on a negative health track before they develop symptoms and incur health costs.

What is a Neural Network?

A neural network is a powerful data modeling tool that is able to capture and represent complex input/output relationships. The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain. Neural networks resemble the human brain in the following two ways:

1. A neural network acquires knowledge through learning.
2. A neural network's knowledge is stored within inter-neuron connection strengths known as synaptic weights.

The true power and advantage of neural networks lies in their ability to represent both linear and non-linear relationships and in their ability to learn these relationships directly from the data being modeled. Traditional linear models are simply inadequate when it comes to modeling data that contains non-linear characteristics.

The most common neural network model is the multilayer perceptron (MLP). This type of neural network is known as a supervised network because it requires a desired output in order to learn. The goal of this type of network is to create a model that correctly maps the input to the output using historical data so that the model can then be used to produce the output when the desired output is unknown.