Knowledge Discovery-Based Analysis of Health Factors of Urinary Infections in Elderly Cardiology Inpatients

Abstract

A set of semantic similarity calculation methods combining full-text text and domain knowledge topics is proposed for the current study of entity association relations such as disease–gene in medical texts combined with topics in knowledge discovery, which is insufficient to reveal the deep semantic association relations of medical domain knowledge at topic level. Taking urinary infections in elderly inpatients as the research subject, word embedding representation of word vectors and topic vectors is performed by the TWE model, and similarity calculation is performed by combining text and domain knowledge topics based on Siamese Network framework. The urinary microbiological culture results of both groups were dominated by Escherichia coli, accounting for 34.65% and 47.92%, respectively; the use of antimicrobial drugs in the symptomatic urinary infection group was 94.19% higher than that in the asymptomatic bacteriuria group, 77.27% ( $x^2$  = 8.158, $P=0.004$ ).