Perception-based Reasoning and Fuzzy Cardinality Provide Direct Measures of Causality Sensitive to Initial Conditions in the Individual Patient(invited Paper)

Background : Clinical trials in medicine use probability -based statistics. Statistics separate the patient’s physiologic elements (specified as variables when given numeric form) from his or her body and define causal correlation for the group. Diagnostic and clinical decisions at the individual patient level are currently based on these definitions of causation. Because data is grouped and averaged, the relationship to initial conditions and their connection to the individual patient is lost. We develop an alternative method that directly measures “causality” in the individual patient that is sensitive to initial conditions. Methods: We define the measure of causal connection between elements in the individual patient. Necessary and Sufficient Causal Ground, Formal Causal Ground and Clinical Causal Effect are derived from the fuzzy subsethood theorem defined by Kosko. From these causal measures, we derive the clinical efficiency measure K from units of fuzzy cardinality. It is how much causal effect is present per unit of a specific patient’s initial conditions. Practically, as “sets as points” in a unit hypercube, each patient is represented as a fuzzy set of defined elements at different points in time. Efficiency K is defined as the extracubal causality measure for any process not represented in the cube acting on the patient between two points in the unit hypercube. For any process, 1/K gives us the dosage necessary of that agent needed to move that specific patient’s initial condition per unit of causal effect. Results: The measures of formal causal ground and clinical causal effect are in units of fuzzy cardinality. Thus the Received by the editors November 27, 2002 / final version received December 6, 2002.