A system and method configured to provide persistent evidence based multi-ontology context dependent decision support, eligibility assessment and feature scoring. Decisions are achieved via a probabilistic functional extension of both potentiality and plausibility towards nouns in all data forms. Plausibility refers to the full set of values garnered by the evidence accumulation process while potentiality is a mechanism to set the various match threshold values. The thresholds define acceptable confidence levels for decision-making and wherein both plausibility and potentiality are implemented through statistical applications which model and estimate the distribution of random vectors by estimating margins and copula separately from all data types. Evidence is filtered by margins and copula on a persistent basis from the scoring of newly harvested content and refined results are computed on the basis of partial matching of feature vector elements for separate and distinct feature weightings associated with the given entity and each of the reference entities within the compressed copula.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A decision support system configured to provide single source and centralized decision making, the decision support system comprising: one or more processors configured to execute computer program modules, the computer program modules comprising: a content harvesting module configured to receive persistent content; a plausibility scoring module configured to perform hypothesis validation and refutation functions and generate a plausibility scoring value; a potentiality scoring module configured to set confidence thresholds for decision making and generate a potentiality scoring value; and a decision determination module configured to adjudicate the potentiality scoring value and the plausibility scoring value as against threshold values and render a decision based thereon. 2. The decision support system of claim 1 wherein said persistent content comprises nouns. 3. The decision support system of claim 1 wherein said persistent content comprises noun based phrases. 4. The decision support system of claim 1 wherein said plausibility scoring value is determined based upon a confidence level related to whether or not said content includes sufficient information to identify said content as well as said content's association with a specified ontology. 5. The decision support system of claim 1 wherein said content is represented as feature vector elements. 6. The decision support system of claim 1 further comprising a reference data storage module, said reference data storage module storing reference data which is matched as against said persistent content. 7. The decision support system of claim 6 wherein said reference data is stored in the form of feature vector elements. 8. The decision support system of claim 1 wherein said plausibility scoring module generates said plausibility scoring value by employing at least one copula function to identify and model applicable dependence structures. 9. The decision support system of claim 1 wherein said potentiality scoring module generates said potentiality scoring value by employing at least one copula function to identify and model applicable dependence structures. 10. The decision support system of claim 1 wherein said decision represents a patient eligibility determination. 11. A computer-implemented method of providing decision support, the method being implemented in a computer system comprising one or more processors configured to execute computer program modules, the method comprising: receiving persistent content; performing hypothesis validation and refutation functions and generating a plausibility scoring value; setting confidence thresholds for decision making and generating a potentiality scoring value; and adjudicating the potentiality scoring value and the plausibility scoring value as against threshold values and rendering a decision based thereon. 12. The method of claim 11 wherein said persistent content comprises nouns. 13. The method of claim 11 wherein said persistent content comprises noun based phrases. 14. The method of claim 11 further comprising the step of determining said plausibility scoring value based upon a confidence level related to whether or not said content includes sufficient information to identify said content as well as said content's association with a specified ontology. 15. The method of claim 11 wherein said content is represented as feature vector elements. 16. The method of claim 11 further comprising the step of storing reference data which is matched as against said persistent content. 17. The method of claim 16 wherein said reference data is stored in the form of feature vector elements. 18. The method of claim 11 wherein said plausibility scoring module generates said plausibility scoring value by employing at least one copula function to identify and model applicable dependence structures. 19. The method of claim 11 wherein said potentiality scoring module generates said potentiality scoring value by employing at least one copula function to identify and model applicable dependence structures. 20. The method of claim 11 wherein said decision represents a patient eligibility determination.