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Automated management of medical data using expert knowledge and applied complexity science for risk assessment and diagnoses

  • US 8,554,580 B2
  • Filed: 10/13/2009
  • Issued: 10/08/2013
  • Est. Priority Date: 10/10/2008
  • Status: Expired due to Fees
First Claim
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1. A method for evaluating medical data of an individual to identify, if any, at-risk medical conditions, comprising:

  • accessing from a memory a medical knowledgebase having a plurality of feature-sets relating to various medical conditions, each of the plurality of feature-sets having a group of highly-associated features relating to the various medical conditions, at least some of said highly-associated features having a plurality of ranges of values, wherein a risk level is assigned for each of the plurality of ranges of values, and wherein the risk level is calculated with a computer for each of the plurality of ranges of values based on associative algorithms acting upon magnitudes of the plurality of ranges of values of the at least some of said highly-associated features within the medical knowledgebase;

    obtaining with the computer the medical data of the individual, the medical data having comprising features of at least one of the various medical conditions, wherein at least some of the features of the medical data of the individual having values;

    identifying with the computer a subset of the plurality of feature-sets in the medical knowledgebase that correlates with the obtained medical data of the individual by correlating at least two of the features of the medical data of the individual with at least two of said highly-associated features of each of the feature-sets in the subset thereby transforming knowledge of the features of the medical data of the individual to metadata in the form of the group of transformed highly-associated features of each of the feature-sets in the subset;

    determining with the computer whether the features of the medical data of the individual are one of normal or abnormal and whether the values of the medical data of the individual are within the magnitudes of the ranges of values of the transformed highly-associated features which are normal or abnormal to interpret relative to a standard as to identify, if any, at-risk medical conditions of said individual;

    assigning at least one risk level to the medical data of the individual based on the determination; and

    outputting from the computer information relating to, if any, the at-risk medical conditions.

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