Can artificial intelligence help a clinical laboratory to draw useful information from limited data sets ? Application to Mixed Connective Tissue Disease

Abstract

Diagnosis is a key step of patient management. During decades, refined decision algorithms and numerical scores based on conventional statistic tools were elaborated to ensure optimal reliability. Recently, a number of machine learning tools were developed and applied to process more and more extensive data sets, including up to million of items and yielding sophisticated classification models. While this approach met with impressive efficiency in some cases, practical limitations stem from the high number of parameters that may be required by a model, resulting in increased cost and delay of decision making. Also, information relative to the specificity of local recruitment may be lost, hampering any simplification of universal models. Here, we explored the capacity of currently available artificial intelligence tools to classify patients found in a single health center on the basis of a limited number of parameters. As a model, the discrimination between systemic lupus erythematosus (SLE) and mixed connective tissue disease (MCTD) on the basis of thirteen biological parameters was studied with eight widely used classifiers. It is concluded that classification performance may be significantly improved by a knowledge-based selection of discriminating parameters.