Data-driven insights into nursing-related cerebral infarction in neurology and endocrinology patients

Background Patients with neurological and hormonal disorders are at a high risk of cerebral infarction (CI) because these conditions adversely affect vascular health. Methods Predictive analytics and evidence-based nursing interventions (PA-E-NI) were used to explore data-driven insights into nursing-related CI management. This study identified crucial risk factors, early warning signs, and optimal nursing interventions (NIs) by analyzing patient records, laboratory results, and real-time monitoring data, thereby enhancing patient outcomes. Variations in blood glucose levels, lipid profiles, hypertension, and coagulation abnormalities were analyzed using machine learning models for stroke risk assessment in endocrine patients. Diabetes mellitus, thyroid dysfunction, and metabolic syndrome are associated with the outcomes. Results Stroke risk can be reduced through early detection using continuous glucose monitoring, blood pressure regulation, and personalized nursing care. Nursing procedures have been revolutionized using an artificial intelligence (AI)-driven decision support system (DSS). AI-driven DSSs may facilitate the detection of ischemic events through AI-assisted imaging and biosensor integration. These systems contribute to reduced hospital readmission rates, enhanced proactive interventions, and improved long-term recovery. Stroke management depends heavily on patient awareness and the implementation of secondary prevention protocols. A multidisciplinary approach is required in neurology and endocrinology settings to facilitate patient-centered stroke care. To optimize real-time decision-making, future studies should focus on integrating electronic health records and wearable health monitoring technologies. Clinicians can enhance CI outcomes through data-driven nursing strategies, thereby enabling more effective and personalized care.