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Through the identification of critical features and ML algorithms, we developed a web application to help clinicians identify high-risk of END after thrombolysis in AIS patients more quickly, easily and accurately as well as making timely clinical decisions.

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This app is designed for risk prediction of early neurological deterioration (END) within 24 hours after thrombolytic therapy in ischemic stroke patients. Users can input various clinical parameters such as time from onset to treatment, white blood cell count, lymphocyte count to monocyte count ratio, hemoglobin level, thrombin time, and prothrombin time. After submitting the inputs, the app utilizes a pre-trained classifier to predict the likelihood of END occurrence. Additionally, the app provides interpretability through SHAP (SHapley Additive exPlanations) values, allowing users to understand the contribution of each input feature to the prediction outcome. This can assist clinicians in making informed decisions regarding patient management and treatment strategies.

See owner's GitHub repository for more information:

This app is based on the paper:

Gao, Y., Zong, C., Liu, H., Zhang, K., Yang, H., Wang, A., Wang, Y., Li, Y., Liu, K., Li, Y., Yang, J., Song, B., & Xu, Y. (2023). Machine Learning-based prediction of Early Neurological Deterioration after Thrombolysis in Acute Ischemic Stroke. medRxiv.

This application was not uploaded by the author, but through their publicly available Github repository,


Warning: Not intended for clinical use. Assume outputs are unsafe and unvalidated. Use carefully.

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