Derivation and External Validation of the "FORECAST-PC" Prediction Scores
This research develops and externally validates the FORECAST-PC machine learning models to predict 90‑day functional outcomes for patients with acute ischemic stroke in the posterior circulation who are selected for endovascular thrombectomy (EVT). The study focuses on creating robust prediction scores that can guide clinical decision‑making and improve patient prognostication. It demonstrates the feasibility of applying advanced analytics to a specific stroke population in the United States.
This research develops and externally validates the FORECAST-PC machine learning models to predict 90‑day functional outcomes for patients with acute ischemic stroke in the posterior circulation who are selected for endovascular thrombectomy (EVT). The study focuses on creating robust prediction scores that can guide clinical decision‑making and improve patient prognostication. It demonstrates the feasibility of applying advanced analytics to a specific stroke population in the United States.
The study exemplifies how machine learning can enhance medical prognostication, supporting Swiss AI efforts to integrate AI into healthcare decision‑making.
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