Prediction of key abnormal biochemical parameters in femoral neck fractures: an AI approach

This study developed several ML-based predictive models that effectively assess changes in perioperative biochemical parameters in patients with femoral neck fractures. The interpretability heatmap clearly indicated the clinical features most influential on each biochemical parameter, such as the close relationship between K* and creatinine, which aligns with kidney regulation mechanisms and existing physiological knowledge. External data validation further demonstrated the model's robustness, suggesting that the model is applicable not only to the existing dataset but also to a broader clinical population. Overall, the proposed model provides an effective tool for perioperative management, with promising potential for clinical practice to help optimise treatment strategies and improve patient outcomes and quality of life.