A Machine Learning Framework for Predicting Nosocomial Escherichia coli Infections in Cervical Cancer
Abstract
Background: This study aimed to characterize the etiological profile of nosocomial infections in cervical cancer patients and to develop a machine learning-based prediction model for infections caused by the predominant pathogen, Escherichia coli, to support clinical decision-making in anti-infective therapy and risk stratification.
Methods: We conducted a retrospective analysis of clinical data from 118 cervical cancer patients to evaluate the distribution and antimicrobial resistance patterns of infectious pathogens. Predictive factors for Escherichia coli infection were identified, and a corresponding prediction model was developed. All the statistical analyses were carried out via R software (version 4.3.2) and iResearch (version 2.9.2).
Results: A total of 151 pathogenic isolates were obtained, with the highest prevalence detected in mid-stream urine samples (69.54%, 105/151). Gram-negative bacteria constituted 76.82% (116/151) of the isolates, among which Escherichia coli was the most frequently identified species (50.33%, 76/151). Antimicrobial susceptibility testing revealed resistance rates exceeding 55% to ceftriaxone, ciprofloxacin, trimethoprim-sulfamethoxazole, and levofloxacin among Escherichia coli isolates, whereas high susceptibility was retained to carbapenems, piperacillin-tazobactam, and amikacin. Logistic regression analysis revealed that Escherichia coli infection was positively associated with earlier clinical stage, absence of anemia, and mid-stream…
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