I really wish you would do something useful like getting survivors recovered. This predicting failure to recover is ABSOLUTELY FUCKING USELESS!
Artificial intelligence to predict individualized outcome of acute ischemic stroke patients: The SIBILLA project
Abstract
Introduction:
Formulating
reliable prognosis for ischemic stroke patients remains a challenging
task. We aimed to develop an artificial intelligence model able to
formulate in the first 24 h after stroke an individualized prognosis in
terms of NIHSS.
Patients and methods:
Seven
hundred ninety four acute ischemic stroke patients were divided into a
training (597) and testing (197) cohort. Clinical and instrumental data
were collected in the first 24 h. We evaluated the performance of four
machine-learning models (Random Forest, K-Nearest Neighbors,
Support Vector Machine, XGBoost) in predicting NIHSS at discharge both
in terms of variation between discharge and admission (regressor
approach) and in terms of severity class namely NIHSS 0–5, 6–10, 11–20,
>20 (classifier approach). We used Shapley Additive exPlanations
values to weight features impact on predictions.
Results:
XGBoost
emerged as the best performing model. The classifier and regressor
approaches perform similarly in terms of accuracy (80% vs 75%) and
f1-score (79% vs 77%) respectively. However, the regressor has higher
precision (85% vs 68%) in predicting prognosis of very severe stroke
patients (NIHSS > 20). NIHSS at admission and 24 hours, GCS at
24 hours, heart rate, acute ischemic lesion on CT-scan and TICI score
were the most impacting features on the prediction.
Discussion:
Our
approach, which employs an artificial intelligence based-tool,
inherently able to continuously learn and improve its performance, could
improve care pathway and support stroke physicians in the communication
with patients and caregivers.
Conclusion:
XGBoost reliably predicts individualized outcome in terms of NIHSS at discharge in the first 24 hours after stroke.
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