Digital healthcare information management
First Claim
1. A method useful in healthcare information management comprising:
- collecting at least one primary element as a snapshot present at the time of recording of health data using at least one collection method selected from one-time, periodic, quasi-periodic and continuous monitoring, and electronically comparing said at least one primary element with at least one reference value to detect changes in said at least one primary element and thereby identify any abnormal or unstable primary element (a first-level, low-resolution analysis); and
analyzing serial changes in said at least one primary element of health data using a dynamic serial analysis and processing unit employing at least one of the following methods selected from mathematical decomposition, mathematical modeling, computer modeling, signal processing, time-series analysis, statistical analysis, and methods of artificial intelligence for assessing changes in serial data, orthogonal decomposition, non-orthogonal decomposition (independent component analysis), multidimensional scaling based on non-metric distances and mapping techniques, non-orthogonal linear mappings, nonlinear mappings and other methods, that make use of projection, re-scaling (change of variables), methods from the theories of singularities, bifurcations, catastrophes, and dynamical systems, and other statistical estimators, linear and nonlinear correlation, analysis of variance, cluster analysis, factor analysis, canonical analysis, regression and discriminant function analyses, and probabilistic methods, Bayesian probability, Bayesian network, Markov model, hidden Markov model, and Mahalanobis distance, pattern recognition, fuzzy logic, neural networks, expert systems, and hybrid artificial intelligence systems to provide detailed characterization of serial changes in any abnormal or unstable primary element (a second-level, higher resolution serial analysis).
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Abstract
System for diagnosis, medical decision support, and healthcare information management that performs analysis of serial health data, adapts to the individual data, and represents dynamics of the most significant parameters (indicators), using at least two scales. The system uses the first-scale (low-resolution) analysis of a snapshot measurement of at least one indicator (primary element) such as heart rate or blood pressure and uses a second-scale (higher-resolution) analysis to determine serial changes in each of the said primary elements. The system optimizes information flow, usage of medical knowledge, and improves accuracy of analysis of serial changes, and adaptability to each individual'"'"'s data. The information can be distributed in parallel to separate databases at different locations.
152 Citations
50 Claims
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1. A method useful in healthcare information management comprising:
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collecting at least one primary element as a snapshot present at the time of recording of health data using at least one collection method selected from one-time, periodic, quasi-periodic and continuous monitoring, and electronically comparing said at least one primary element with at least one reference value to detect changes in said at least one primary element and thereby identify any abnormal or unstable primary element (a first-level, low-resolution analysis); and analyzing serial changes in said at least one primary element of health data using a dynamic serial analysis and processing unit employing at least one of the following methods selected from mathematical decomposition, mathematical modeling, computer modeling, signal processing, time-series analysis, statistical analysis, and methods of artificial intelligence for assessing changes in serial data, orthogonal decomposition, non-orthogonal decomposition (independent component analysis), multidimensional scaling based on non-metric distances and mapping techniques, non-orthogonal linear mappings, nonlinear mappings and other methods, that make use of projection, re-scaling (change of variables), methods from the theories of singularities, bifurcations, catastrophes, and dynamical systems, and other statistical estimators, linear and nonlinear correlation, analysis of variance, cluster analysis, factor analysis, canonical analysis, regression and discriminant function analyses, and probabilistic methods, Bayesian probability, Bayesian network, Markov model, hidden Markov model, and Mahalanobis distance, pattern recognition, fuzzy logic, neural networks, expert systems, and hybrid artificial intelligence systems to provide detailed characterization of serial changes in any abnormal or unstable primary element (a second-level, higher resolution serial analysis). - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A method useful in healthcare information management comprising:
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analyzing at least one primary element of health data by electronically comparing a snapshot of data recorded respecting said at least one primary element with at least one reference value to detect changes in such primary element and thereby identify any abnormal or unstable primary element (a first-level, low resolution analysis); analyzing serial changes in said at least one primary element using a dynamic serial analysis and processing unit employing at least one of the following methods selected from mathematical decomposition, mathematical modeling, computer modeling, signal processing, time-series analysis, statistical analysis, and methods of artificial intelligence for assessing changes in serial data, orthogonal decomposition, non-orthogonal decomposition (independent component analysis), multidimensional scaling based on non-metric distances and mapping techniques, non-orthogonal linear mappings, nonlinear mappings and other methods, that make use of projection, re-scalin (change of variables), methods from the theories of singularities, bifurcations, catastrophes, and dynamical systems, and other statistical estimators, linear and nonlinear correlation, analysis of variance, cluster analysis, factor analysis, canonical analysis, regression and discriminant function analyses, and probabilistic methods, Bayesian probability, Bayesian network, Markov model, hidden Markov model, and Mahalanobis distance, pattern recognition, fuzzy logic, neural networks, expert systems, and hybrid artificial intelligence systems to provide detailed characterization of any serial changes in abnormal or unstable primary element (a second-level, higher resolution analysis); and analyzing changes in said at least one primary elements in a third level resolution, using at least one dynamic analysis and processing unit, which includes combining the analysis of primary elements with digitized personal health data. - View Dependent Claims (15, 16, 17, 18, 19, 20, 21, 22, 23, 24)
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25. A system useful in healthcare information management comprising:
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a first analysis and processing unit for analyzing a snapshot of at least one of a plurality of primary elements from recorded health data and processing said at least one primary element to generate data respecting said at least one primary element, and comparing at least one reference value respecting said at least one primary element with data newly received by said first analysis and processing unit and producing at least one indicator respecting any differences between said at least one reference value and said newly received data (a low resolution analysis), a second analysis and processing unit for processing health data collected over time using at least one of the following methods selected from mathematical decomposition, mathematical modeling, computer modeling, signal processing, time-series analysis, statistical analysis, and methods of artificial intelligence for assessing changes in serial data, orthogonal decomposition, non-orthogonal decomposition (independent component analysis), mathematical modeling, computer modeling, signal processing, time-series analysis, statistical analysis, multidimensional scaling based on non-metric distances and mapping techniques, non-orthogonal linear mappings, nonlinear mappings and other methods that make use of projection, re-scaling (change of variables), methods from the theories of singularities, bifurcations, catastrophes, and dynamical systems, and other statistical estimators, linear and nonlinear correlation, analysis of variance, cluster analysis, factor analysis, canonical analysis, regression and discriminant function analyses, and probabilistic methods, Bayesian probability, Bayesian network, Markov model, hidden Markov model, and Mahalanobis distance, pattern recognition, fuzzy logic, neural networks, expert systems, and hybrid artificial intelligence systems to detect serial changes in said at least one primary element (higher resolution analysis); and a communications unit for exchanging information between said first and second analysis and processing units. - View Dependent Claims (26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45)
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- 46. A system useful in healthcare information management comprising a dynamic serial analysis and processing unit for processing health data collected over time using at least one of the following methods selected from mathematical decomposition, mathematical modeling, computer modeling, signal processing, time-series analysis, statistical analysis, and methods of artificial intelligence for assessing changes in serial data, orthogonal decomposition, non-orthogonal decomposition (independent component analysis), multidimensional scaling based on non-metric distances and mapping techniques, non-orthogonal linear mappings, nonlinear mappings and other methods, that make use of projection, re-scaling (change of variables), methods from the theories of singularities, bifurcations, catastrophes, and dynamical systems, and other statistical estimators, linear and nonlinear correlation, analysis of variance, cluster analysis, factor analysis, canonical analysis, regression and discriminant function analyses, and probabilistic methods, Bayesian probability, Bayesian network, Markov model, hidden Markov model, and Mahalanobis distance, pattern recognition, fuzzy logic, neural networks, expert systems, and hybrid artificial intelligence systems to detect serial changes in said at least one primary element (serial analysis) to provide detailed characterization of any abnormal or unstable primary elements.
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49. A method useful in healthcare information management comprising:
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collecting at least one primary element over a period of time; and analyzing serial changes in said at least one primary element of health data using a dynamic serial analysis and processing unit employing at least one of the following methods selected from mathematical decomposition, mathematical modeling, computer modeling, signal processing, time-series analysis, statistical analysis, and methods of artificial intelligence for assessing changes in serial data, orthogonal decomposition, non-orthogonal decomposition (independent component analysis), multidimensional scaling based on non-metric distances and mapping techniques, non-orthogonal linear mappings, nonlinear mappings and other methods, that make use of projection, re-scaling (change of variables), methods from the theories of singularities, bifurcations, catastrophes, and dynamical systems, and other statistical estimators, linear and nonlinear correlation, analysis of variance, cluster analysis, factor analysis, canonical analysis, regression and discriminant function analyses, and probabilistic methods, Bayesian probability, Bayesian network, Markov model, hidden Markov model, and Mahalanobis distance, pattern recognition, fuzzy logic, neural networks, expert systems, and hybrid artificial intelligence systems to provide detailed characterization of serial changes in any abnormal or unstable primary element. - View Dependent Claims (50)
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Specification