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Stroke mortality prediction using machine learning: systematic review.

Lihi Schwartz¹, Roi Anteby², Eyal Klang³

¹Sheba Medical Center, Tel Hashomer, Israel, and Ben-Gurion University of the Negev, Be'er Sheva, Israel.

Journal of the Neurological Sciences

|December 29, 2022

Summary

This summary is machine-generated.

Machine learning models show promise for predicting stroke mortality, identifying age, BMI, and NIHSS score as key factors. However, current studies have high bias and heterogeneity, necessitating further research.

Keywords:

Artificial intelligence Machine learning Mortality prediction Mortality prognostic factors Stroke

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Area of Science:

Neurology
Medical Informatics
Data Science

Background:

Accurate stroke prognostication is vital for effective therapy and rehabilitation.
Machine learning (ML) algorithms are increasingly used for predicting stroke outcomes.

Conclusions:

ML models utilizing admission data can aid in stroke mortality prediction.
Current evidence is limited by high bias and heterogeneity.
Prospective, multicenter studies with standardized reporting are needed to validate ML algorithms for stroke prognostication.