This study aimed to determine the feasibility and relative performance of logistic regression and artificial neural network models for the prediction of MRSA infection in surgical patients using a range of demographic, clinical, procedural, and hospital-related factors. The results showed that both algorithms are effective modeling approaches with reasonable performance and suggest that a clinical decision support tool based on either model could be informative in clinical practice.

Describe the new knowledge and additional skills the participant will gain after attending your presentation.: The poster presentation will help participants understand the advantages and disadvantages of two machine learning modeling approaches (logistic regression and artificial neural network) for the prediction of MRSA infection in surgical patients. The utility of each model in support of clinical decision support systems will also be described.


Kevin Wilson (Presenter)
RTI International

Shankar Srinivasan, Rutgers Biomedical and Health Sciences

Presentation Materials: