Surgical site infections (SSIs) affect 2–20% of surgical procedures and are a major source of postoperative morbidity, prolonged hospitalization, readmission, mortality, and healthcare costs, making prevention a key priority. Existing prediction tools such as the NNIS index and SENIC score depend on a limited set of clinical variables including wound class, ASA status, and operative duration, while failing to capture complex interactions among patients, surgeons, and comorbidities. To address this limitation, we propose a graph attention network (GAT) framework that represents each surgical case as a heterogeneous graph composed of patient, surgeon, and comorbidity nodes, with intraoperative variables included as features and attention mechanisms used to learn the most influential relationships. This approach models relational dependencies such as the interaction between surgeon experience, patient conditions, and comorbidity combinations, enabling more accurate and context-aware SSI risk prediction to support personalized preventive interventions.
Clinical Intelligence Research Press