Automated health data acquisition, processing and communication system and method

A computer-implemented system for integrating modulated output from each of a plurality of models to quantify factors to generate a plurality of values, each within a continuous distribution, and selecting a respective discrete category associated with each of the values to represent a likelihood of a future occurrence, the system comprising:

• a first data model running on a computing device that quantifies each of a plurality of endpoints that contribute to a current state and the likelihood of the future occurrence, wherein each quantified value of the respective quantified endpoints is calculated within a continuous distribution;
• a second data model running on at least one computing device that generates respective values representing aspects of at least one present condition that impacts the likelihood of the future occurrence, wherein each of the respective generated values is within a continuous distribution;
• a third data model running on the at least one computing device that identifies individual ones of a plurality of factors associated with a subset of the endpoints and/or the aspects that are individually modifiable, and generating a value within a continuous distribution representing each of the plurality of factors;
• a modulating model running on the at least one computing device that modulates at least one value quantified by the first data model, generated by the second data model and/or identified by the third data model, to scale a value representing at least one aspect associated with the likelihood of the future occurrence, wherein the modulating is based on at least one factor derived from at least one of the first data model, the second data model, and the third data model;
• and
• at least one of artificial intelligence and machine learning comprised in the at least one computing device that integrates at least two of the values associated with each of the plurality of continuous distributions, and selects a respective discrete category associated with the integrated values, wherein each of the integrated values and the selected category represent the likelihood of a future occurrence.

A computer-implemented method for integrating modulated output from each of a plurality of models to quantify factors to generate a plurality of values, each within a continuous distribution, the method comprising:

• quantifying, by a first data model running on a computing device, each of a plurality of endpoints that contribute to a current state and the likelihood of the future occurrence, wherein each quantified value of the respective quantified endpoints is calculated within a continuous distribution;
• generating, by a second data model running on at least one computing device, respective values representing aspects of at least one present condition that impacts the likelihood of the future occurrence, wherein each of the respective generated values is within a continuous distribution;
• identifying, by a third data model running on the at least one computing device, individual ones of a plurality of factors associated with a subset of the endpoints and/or the aspects that are individually modifiable, and generating a value within a continuous distribution representing each of the plurality of factors;
• and
• modulating, by a modulating model running on the at least one computing device, at least one value quantified by the first data model, generated by the second data model and/or identified by the third data model to scale a value representing at least one aspect associated with the likelihood of the future occurrence, wherein the modulating is based on at least one factor derived from at least one of the first data model, the second data model, and the third data model.