Does an AI-based multimodal risk assessment model (AMRAMS) improve accuracy in predicting surgical site infections compared to conventional machine learning methods and the NNIS risk index?
An AI-based multimodal risk assessment model using EMR data and deep learning improves the accuracy of predicting surgical site infections compared to the traditional NNIS risk index.
Our AMRAMS based on EMR data and deep learning methods-CNN and self-attention network-had significant advantages in terms of accuracy compared with other conventional machine learning methods and the NNIS risk index. Moreover, the semantic embeddings of preoperative notes improved the model performance further. Our models could replace the NNIS risk index to provide personalized guidance for the preoperative intervention of SSIs. Through this case, we offered an easy-to-implement solution for building multimodal RAMs for other similar scenarios.
Chen et al. (2020) studied this question.