The Use of Censors and Reasoning by Analogy to Aid in the Diagnosis of Thyroid Diseases

Abstract

A patient rarely has a single, isolated disease. The situation is usually much more complex since the different parts of the human organism and metabolism interact with each other on multiple levels and follow several feedback patterns. These interactions and feedback patterns become more important with the addition of the external environment. When several diseases are present, the first steps of a medical diagnosis should be to research and determine whether one of the diseases interacts with (“Censors”) or changes the significant symptoms, syndromes, or the results of the laboratory tests of the other disease. We will try, within this paper, to go beyond the scope of the first generation of Artificial Intelligence systems in medicine to see the effect of two diseases on each other. One important part of the effect of two diseases on each other is the entrancing effect of what we call “Censors.” In addition, causal reasoning, reasoning by analogy, and learning from precedents are important and necessary for a human-like expert in medicine. Their application to thyroid diseases, with an implemented system, are considered in this paper.