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

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 (...

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Bibliographic Details
Main Author: Muhammad Mohsin Khan (22150360) (author)
Other Authors: 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)
Published: 2025
Subjects:
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
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