This project focuses on enhancing the care of ischemic stroke patients treated with fibrinolysis and thrombectomy by developing AI-driven prognostic models. Ischemic stroke, caused by a blood clot blocking cerebral blood flow, is often managed through these critical interventions.
The project’s innovation lies in leveraging advanced natural language processing (NLP) techniques to extract clinical information from unstructured electronic health records. The AI models analyze these data alongside a wide range of clinical and imaging variables to identify patterns and risk factors associated with unfavorable outcomes.
The resulting predictive models assist clinicians in evaluating the risks and benefits of treatments for individual patients. For example, high-risk patients identified by the model could receive alternative therapies or enhanced post-treatment care, enabling more informed and personalized decision-making.
By integrating these AI tools into stroke care pathways, the project aims to transform treatment approaches, reduce complications, and optimize recovery for ischemic stroke patients. This represents a significant advancement in precision medicine for neurological care, improving patient outcomes through data-driven insights.