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

A computer system for evaluating medical data of an individual to identify an at-risk medical condition, said computer system comprising a central processing unit coupled to a memory, said central processing unit being programmed to:

• access from the 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 as risk level is assigned for each of the plurality of ranges of values, and wherein the risk level is calculated by the central processing unit 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;
• obtain the medical data of the individual 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;
• identify 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 of the group of transformed highly-associated features of each of the feature-sets in the subset;
• determine 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;
• assign at least one risk level to the medical data of the individual based on the determination;
• and
• output from an interface information relating to, if any, the at-risk medical conditions.

A non-transitory computer-readable storage medium with an executable program stored thereon, said program evaluating medical data of an individual, said medium located on a computer system wherein said program instructs a central processing unit coupled to a memory and to a data receiving interface to perform the following steps:

• access from the 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 by the central processing unit 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;
• obtain the medical data of the individual, said medical data having 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;
• identify 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;
• determine 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;
• assign at least one risk level to the medical data of said individual based on the determination;
• and
• output from interface information relating to the at-risk medical conditions.