Application and prospect of bioinformatics in the prevention and treatment of ischemic stroke: A comprehensive narrative review.

Ischemic stroke (IS) is a leading cause of death and disability globally, with current clinical management facing bottlenecks such as inadequate early recognition, subjective imaging interpretation, narrow therapeutic time windows, and imprecise prognostic assessment. Bioinformatics, an interdisciplinary field integrating life sciences, information technology and statistics, has emerged as a critical solution to these challenges, yet a review of its full-cycle application in IS management remains lacking. This study elaborates on the application of bioinformatics across the entire clinical pathway of IS, from prevention to prognosis, and clarifies its technical foundations, practical applications, existing challenges, and future directions. First, the paper expounds on the core bioinformatics technologies (machine learning, deep learning, and computer-aided drug design) and three types of core medical data (imaging, electronic health records, and multi-omics) applied in IS research, and summarizes their evaluation metrics and clinical application characteristics. Second, it details the application of bioinformatics in primary/secondary prevention of IS (risk assessment, early warning, recurrence prediction, and medication guidance), diagnosis and treatment (early identification, clinical diagnosis, and treatment decision support), as well as prognosis prediction and intelligent rehabilitation (functional outcome prediction, complication risk assessment, and personalized rehabilitation planning). The research shows that bioinformatics technologies can construct high-precision predictive models by integrating multi-modal data, realize precise risk stratification and early warning, optimize clinical diagnosis and treatment decision-making, and build closed-loop intelligent rehabilitation management systems-all of which have been validated in numerous clinical studies with promising performance. Finally, the paper points out the core challenges faced by the clinical translation of bioinformatics in IS research, including data gaps, poor model interpretability, and insufficient prospective validation.