Machine learning model for assessment of risk factors and postoperative day for superficial v.s deep/organ-space surgical site infections

Deep and organ space surgical site infections (SSI) require more intensive treatment, cause more clinically severe disease and may have different risk factors compared to superficial SSIs. Machine learning (ML) algorithms may be used to analyze multiple factors for prediction of type and time of development of SSI. Therefore, this study developed a ML model to predict type and postoperative week of SSI, and compared risk factors for superficial and deep SSIs after general surgery procedures. : ML models offer reasonable accuracy in prediction of superficial vs deep SSI and time of developing infection. Follow-up duration and allocation of treatment strategies can be informed by ML predictions. Risk factors for superficial SSI and deep/organ- space SSI vary in terms of magnitude and significance.

This research-based project aims to ensure the well-being of patients in post-operative care which is prone to being ignored. The ML model ensures economic growth and development in the medical equipment industry in Pakistan. Currently RCAI is seeking collaborators whose goal is to develop innovative solutions nationally.