{Reference Type}: Journal Article
{Title}: Risk assessment and prediction of nosocomial infections based on surveillance data using machine learning methods.
{Author}: Chen Y;Zhang Y;Nie S;Ning J;Wang Q;Yuan H;Wu H;Li B;Hu W;Wu C;
{Journal}: BMC Public Health
{Volume}: 24
{Issue}: 1
{Year}: 2024 Jul 4
{Factor}: 4.135
{DOI}: 10.1186/s12889-024-19096-3
{Abstract}: <B>BACKGROUND: </B>Nosocomial infections with heavy disease burden are becoming a major threat to the health care system around the world. Through long-term, systematic, continuous data collection and analysis, Nosocomial infection surveillance (NIS) systems are constructed in each hospital; while these data are only used as real-time surveillance but fail to realize the prediction and early warning function. Study is to screen effective predictors from the routine NIS data, through integrating the multiple risk factors and Machine learning (ML) methods, and eventually realize the trend prediction and risk threshold of Incidence of Nosocomial infection (INI).<BR><B>METHODS: </B>We selected two representative hospitals in southern and northern China, and collected NIS data from 2014 to 2021. Thirty-nine factors including hospital operation volume, nosocomial infection, antibacterial drug use and outdoor temperature data, etc. Five ML methods were used to fit the INI prediction model respectively, and to evaluate and compare their performance.<BR><B>RESULTS: </B>Compared with other models, Random Forest showed the best performance (5-fold AUC = 0.983) in both hospitals, followed by Support Vector Machine. Among all the factors, 12 indicators were significantly different between high-risk and low-risk groups for INI (P < 0.05). After screening the effective predictors through importance analysis, prediction model of the time trend was successfully constructed (R2 = 0.473 and 0.780, BIC = -1.537 and -0.731).<BR><B>CONCLUSIONS: </B>The number of surgeries, antibiotics use density, critical disease rate and unreasonable prescription rate and other key indicators could be fitted to be the threshold predictions of INI and quantitative early warning.