Risk Factors for Perinatal Arterial Ischemic Stroke: A Machine Learning Approach.
Ratika SrivastavaLauran ColeKimberly AmadorNils Daniel ForkertMary J DunbarMichael I ShevellMaryam OskouiAnna Purna BasuMichael J RivkinEilon ShanyLinda S de VriesDeborah DeweyNicole LetourneauPauline MouchesMichael D HillAdam KirtonPublished in: Neurology (2024)
Machine learning may be an alternative, unbiased method to identify clinical predictors associated with PAIS. Identification of previously suggested and novel clinical factors requires cautious interpretation but supports the multifactorial nature of PAIS pathophysiology. Our results suggest that identification of neonates at risk of PAIS is possible.