Computational method and system to perform empirical induction
First Claim
1. A computational method to perform empirical induction, the method comprised of utilizing a computer or computer system programmed to:
- convert any dimensional series of repeated measures data into sets of dichotomous series, the data being obtained by repeatedly measuring attributes or events for an individual entity or the individual'"'"'s environment on two or more occasions over an interval of time, at least one variable functioning as an independent variable and being used to define independent events and at least one variable functioning as a dependent variable and being used to define dependent events;
apply at least one feature to any dichotomous series or any set of dichotomous series to form additional dichotomous series of events that may be associated longitudinally, wherein the at least one feature is selected from the group consisting of episode length, episode criterion, persistence, Boolean events, Boolean event scope, Boolean event criterion, delay after Boolean events, and persistence after Boolean events;
compute a longitudinal association score (LAS) for each selected combination of one dichotomous series of independent events with one dichotomous series of dependent events, each LAS and any array of LASs being descriptive of the amount of evidence and the positive or negative direction of any longitudinal association that may obtain between the independent variable(s) and the dependent variable(s) for the individual.
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Abstract
The present invention is an improved computational method and system of empirical induction that can be used to arrive at generalized conclusions and make predictions involving longitudinal associations between and among variables and events. Empirical induction is used to gain scientific knowledge, to develop and evaluate treatments and other interventions, and to help make predictions and decisions. The invention, which is distinct from and often complementary to the statistical method, is applied to repeated measures and multiple time-series data and can be used to quantify, discover, analyze, and describe longitudinal associations for individual real and conceptual entities. Major improvements include provisions to define Boolean independent events and Boolean dependent events and to apply analysis parameters such as episode length and episode criterion for both independent and dependent variables, persistence after independent events, and delay and persistence after Boolean independent events. These improvements are in addition to levels of independent and dependent variables, delay after independent events, and provision to quantify benefit and harm across two or more dependent variables. Additional improvements include provisions to quantify longitudinal associations as functions of period or time and to compute values of predictive indices when there are two or more independent variables. Major applications and uses of the invention include data mining, the conduct of clinical trials of treatments for the management or control of chronic disorders, health-effect monitoring, the quantification and analysis internal control in adaptive systems, analyses of serial functional images, analyses of behavior and behavior modification, and use to create computerized devices and systems whose behavior can be modified by experience. The present invention is best implemented on the Internet.
279 Citations
104 Claims
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1. A computational method to perform empirical induction, the method comprised of utilizing a computer or computer system programmed to:
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convert any dimensional series of repeated measures data into sets of dichotomous series, the data being obtained by repeatedly measuring attributes or events for an individual entity or the individual'"'"'s environment on two or more occasions over an interval of time, at least one variable functioning as an independent variable and being used to define independent events and at least one variable functioning as a dependent variable and being used to define dependent events;
apply at least one feature to any dichotomous series or any set of dichotomous series to form additional dichotomous series of events that may be associated longitudinally, wherein the at least one feature is selected from the group consisting of episode length, episode criterion, persistence, Boolean events, Boolean event scope, Boolean event criterion, delay after Boolean events, and persistence after Boolean events;
compute a longitudinal association score (LAS) for each selected combination of one dichotomous series of independent events with one dichotomous series of dependent events, each LAS and any array of LASs being descriptive of the amount of evidence and the positive or negative direction of any longitudinal association that may obtain between the independent variable(s) and the dependent variable(s) for the individual. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53)
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54. A computer system to perform empirical induction, the system comprising:
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means for converting any dimensional series of repeated measures data into sets of dichotomous series, the data being obtained by repeatedly measuring attributes or events for an individual entity or the individual'"'"'s environment on two or more occasions over an interval of time, at least one variable functioning as an independent variable and being used to define independent events and at least one variable functioning as a dependent variable and being used to define dependent events;
means for applying at least one feature to any dichotomous series or any set of dichotomous series to form additional dichotomous series of events that may be associated longitudinally, wherein the at least one feature is selected from the group consisting of episode length, episode criterion, persistence, Boolean events, Boolean event scope, Boolean event criterion, delay after Boolean events, and persistence after Boolean events;
means for computing a LAS for each selected combination of one dichotomous series of independent events with one dichotomous series of dependent events, each LAS and any array of LASs being descriptive of the amount of evidence and the positive or negative direction of any longitudinal association that may obtain between the independent variable(s) and the dependent variable(s) for the individual. - View Dependent Claims (55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104)
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Specification