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A Cognitive Machine-Learning System to Discover Syndromes in Erythemato-Squamous Diseases
Abstract
A syndrome is a set of typical clinical features that appear together often enough to suggest they may represent a single, as yet unknown, disease. The discovery of syndromes and relative taxonomy formation is the critical early phase of the process of scientific discovery in the medical domain. The author proposes a machine learning system to discover syndromes (seen as prototypes of clinical cases) that is based on the Eleanor Rosch's prototype theory on how the human mind categorizes and infers prototypes from observations. A comparison on a case study in erythemato-squamous diseases of the proposed system against three hierarchical clustering algorithms shows that the system obtains performances which are averagely better. The system implemented can be considered a “scientific discovery support system” because it can discover unknown syndromes to the advantage of research activities and syndromic surveillance.
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