Toward Grading Subarachnoid Hemorrhage Risk Prediction: A Machine Learning-Based Aneurysm Rupture Score.

Malik, Khalid; Alam, Fakhare; Santamaria, Jeremy; Krishnamurthy, Madan; Malik, Ghaus

Malik, Khalid; Alam, Fakhare; Santamaria, Jeremy; Krishnamurthy, Madan; Malik, Ghaus.

Afiliación

Malik K; Department of Computer Science & Engineering, School of Engineering and Computer Science, Oakland University, Rochester, Michigan, USA.
Alam F; Department of Computer Science & Engineering, School of Engineering and Computer Science, Oakland University, Rochester, Michigan, USA.
Santamaria J; Oakland University, William Beaumont School of Medicine, Rochester, Michigan, USA.
Krishnamurthy M; Department of Computer Science & Engineering, School of Engineering and Computer Science, Oakland University, Rochester, Michigan, USA.
Malik G; Department of Neurosurgery, Henry Ford Hospital, Detroit, Michigan, USA. Electronic address: gmalik1@hfhs.org.

World Neurosurg ; 172: e19-e38, 2023 Apr.

Article en En | MEDLINE | ID: mdl-36410705

Asunto(s)

Aneurisma Roto; Aneurisma Intracraneal; Hemorragia Subaracnoidea; Humanos; Hemorragia Subaracnoidea/cirugía; Inteligencia Artificial; Aneurisma Intracraneal/cirugía; Espacio Subaracnoideo; Factores de Riesgo; Aneurisma Roto/cirugía; Aprendizaje Automático

Palabras clave

Association mining; Intracranial aneurysms; Machine learning; PHASES score; Subarachnoid hemorrhage; UIATS score

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Hemorragia Subaracnoidea / Aneurisma Intracraneal / Aneurisma Roto Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: World Neurosurg Asunto de la revista: NEUROCIRURGIA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

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