Prediction of nosocomial infections associated with surgical interventions

Nosocomial infections represent an ongoing challenge to healthcare quality and patient safety, negatively impacting clinical outcomes and increasing the burden on healthcare systems. Thus, controlling this type of infection plays a very important role in ensuring a better quality of life for patients. Although the control and prevention measures for these infections are well defined, their signaling and detection is carried out manually and sometimes late, which compromises the health status of patients and everyone around them. In this context, this study emerged with the aim of exploring the potential of data mining techniques to predict the occurrence of nosocomial infections, with a specific focus on infections associated with surgical interventions. Using datasets for the period between 2018 and 2022, sourced from a Portuguese hospital and duly anonymized to protect patient privacy, several classification algorithms and data balancing techniques were analyzed to deal with the uneven nature of the data and the presence of minority classes. Among the algorithms and balancing techniques used, it was found that the Random Forest algorithm combined with the Oversampling technique showed superior performance in identifying cases of nosocomial infections associated with surgical interventions. The results of this study highlight the importance of collaboration between medicine and technology, indicating that the integration of data mining techniques can prove to be valuable tools to improve clinical decision-making and infection management in surgical context.