Intelligent clinical decision support for small patient datasets

Abstract

The ways of substantiating the clinical decision of doctors in the absence of clinical treatment protocols are considered. A comparative evaluation of various statistical methods for ranking clinical symptoms in terms of significance for predicting the outcome of the disease in a small sample of patients with COVID-19 and a history of cardiovascular diseases was performed. The data set (141 patients, 81 factors) was formed based on the materials of electronic medical records of patients of the Federal State Budgetary Institution “National Medical Research Center named after V.A. Almazov”. A subset of controllable risk factors (51 factors) was identified. Descriptive statistics methods (one-way ANOVA, Mann-Whitney and χ² tests) and dimensionality reduction methods (univariate linear regression combined with multiple logistic regression, generalized discriminant analysis, and various decision tree algorithms) were used to rank the factors. To compare the ranking results and evaluate the statistical stability, Kendall’s correlation was used, visualized as a heat map and a positional graph. It has been established that the use of descriptive statistics methods is justified when ranking on a small sample size of patients. It is shown that the ensemble of ranking results may be statistically inconsistent. It is concluded that the positions of the same features obtained by ranking them as part of a complete set and a subset of features do not match; therefore, when choosing a statistical processing method for expert evaluation, one should take into account the meaningful formulation of the problem. It is shown that the statistical stability of ranking under conditions of small samples depends on the number of features taken into account, and this dependence is significantly different for different ranking methods. The proposed method of intellectual support and verification of clinical decisions in terms of choosing the most significant clinical signs can be used to select and justify the tactics of managing patients in the absence of clinical protocols.

Keywords

• clinical decision support
• clinical expertise
• feature ranking
• small cohorts
• statistical methods