Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes

<p dir="ltr">Accurately predicting the severity of subarachnoid hemorrhage (SAH) is critical for informing clinical decisions and improving patient outcomes. This study addresses the challenges of imbalanced data in SAH severity classification by employing the Modified Rankin Scale (...

وصف كامل

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Muhammad Mohsin Khan (22150360) (author)
مؤلفون آخرون:	Adiba Tabassum Chowdhury (19444792) (author), Md. Shaheenur Islam Sumon (17983810) (author), Shaikh Nissaruddin Maheboob (22565696) (author), Arshad Ali (638656) (author), Abdul Nasser Thabet (22565699) (author), Ghaya Al-Rumaihi (22565702) (author), Sirajeddin Belkhair (17151106) (author), Ghanem AlSulaiti (22565705) (author), Ali Ayyad (149042) (author), Noman Shah (22150363) (author), Anwarul Hasan (1332066) (author), Shona Pedersen (2792278) (author), Muhammad E. H. Chowdhury (14150526) (author)
منشور في:	2025
الموضوعات:	Biomedical and clinical sciences Neurosciences Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning Subarachnoid hemorrhage Severity prediction Modified rankin scale Imbalanced data Electronic health record
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes

مواد مشابهة