Discussion
sepsis, septic shock
SEPSIS, SEPTIC SHOCK
We developed an AI prediction model using MLP for ED patients with hyperglycemic crises that provided real-time decision-making assistance to physicians. The AUC of the model was 0.852 for sepsis or septic shock, 0.743 for ICU admissions, and 0.796 for all-cause mortality within 1 month. The impact study showed that the AI group tended to have lower ICU admissions and all-cause mortality than the non-AI group, but the differences were not significant.Clinical decision rules (CDRs) like the PHD score can help with critical decision-making regarding patient health [AI is a breakthrough in healthcare that has the potential to improve the system. MLP, a significant model in the artificial neural network, is preferred for solving nonlinear problems. It consists of the input, hidden, and output layers and mimics the human brain [We integrated an AI prediction model into the HIS, which overcame barriers between AI research and clinical practice, but there were implementation barriers. Hospital policies and cooperation from the hospital information department were crucial for successful implementation. Additionally, incorporating AI into the HIS was technically challenging and may require overhauling existing information technology systems. Finally, concerns regarding malpractice, accuracy, and physician replacement by AI may affect physician acceptance of AI implementation [Based on the same dataset, the AUC of all-cause mortality of the best model in our study was superior to that of the PHD score (0.796 vs. 0.693), suggesting that our AI model may be a better tool for predicting adverse outcomes in ED patients with hyperglycemic crises than the conventional PHD score.We used the AUC, a recognized and comprehensive metric, to select the algorithm for our study [We used the SHAP value, a new method to increase the transparency of AI prediction, to identify the importance of each feature variable for determining adverse outcomes [The study implemented a real-time AI prediction model integrated in the HIS to predict adverse outcomes in ED patients with hyperglycemic crises, which was a major strength. However, there were some limitations. The AUC for predicting ICU admission was lower than that for sepsis or septic shock and all-cause mortality, possibly due to the subjective nature of ICU admission decision-making [