Modeling Causal Relationships: The value of case-based discovery

Robert Fredericks rfredericks@icloud.com

Abstract

The ethical practice of medicine is founded upon the exploration of outliers while engaging the findings of basic science and the narrative of the patient that can be characterized as the anthropologic perspective. This contrasts with the business model of risk factor management. In the course of practice, it is possible to build a coherent narrative that organizes data, facilitates education and informs scientific hypotheses, all of which are marginalized and devalued by business models that rely on non-causal associations. Human beings, the subject of anthropologic investigation, value a coherent story that is paradoxically considered noise in the calculation of risk. Utilizing cases demonstrating exceptions to the expected outcomes as a means to expose the nature of causal relationships, we reported the discovery of interventions effective in the induction of remission in Graves Disease at MedX 2016 in a poster titled “Authenticity: The Ruby Red Slippers.” It strongly supported a coherent model of metabolism that has general applicability. This work has been extended due to the recognition that the heterozygotic carriers of mutations causative of hemochromatosis, when homozygous, rather than being innocuous demonstrate sexually dimorphic phenotypes that confer selective advantage during reproductive years, explaining retention in the genome. Modelling of the causal relationships influenced by the carrier state exposes disruptive influences on health expressed in highly pleiotropic phenotypes that can be recognized in the course of a metabolic evaluation that includes attention to narrative descriptions of fatigue and disordered thermal adaptation. The models have been validated through the identification of the carrier state in 15 of 20 cases suspected where the highest random expectation would be no more than 1 in 10 as seen in northern Europeans. The anthropologic approach captures data and organizes it in a manner that is very unlikely to be accomplished by systems engaging artificial intelligence alone. The structural models include coherent explanation of the transition from the aquatic to the terrestrial environment as crucial for the understanding of metabolic diseases and associated behavioral manifestations that are readily understood by the patients. The value of authentic modeling of causal relationships to the appropriate application of artificial intelligence as an assistant to ethical practice and the potential to refine risk management strategies emphasizes the use of narrative and the role of the patient-physician relationship in the discovery of biologic principles that are the foundation of health and the development of an affordable care system.       
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