Analysis of 20,000 Patients with Acute Ischemic Stroke treated with Endovascular Thrombectomy using Machine Learning Clustering Algorithm (P12-13.006)

This study employs machine learning-based clustering algorithms to identify unique clinical patient profiles with Acute Ischemic Stroke (AIS) treated with Endovascular Thrombectomy (EVT), providing insights into the stratification of risk factors and treatment outcomes.

Authors

Rafay A. Khan (ORCID: https://orcid.org/0000-0002-6146-5800)
Aryan Malhotra
Adam Kiss (ORCID: https://orcid.org/0000-0001-7442-4250)
Fawaz Al‐Mufti (ORCID: https://orcid.org/0000-0003-4461-7005)
Mateusz Faltyn (ORCID: https://orcid.org/0000-0002-7276-3029)

Institutions

Westchester Medical Center (US)
New York Medical College (US)

Publication Details

Journal: Neurology
Published: 2025-04-07
DOI: https://doi.org/10.1212/wnl.0000000000212672
Primary Topic: Acute Ischemic Stroke Management
Type: article
Field-Weighted Citation Impact: 0.00

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article

Analysis of 20,000 Patients with Acute Ischemic Stroke treated with Endovascular Thrombectomy using Machine Learning Clustering Algorithm (P12-13.006)

Rafay A. Khan, Aryan Malhotra, Adam Kiss, Fawaz Al‐Mufti et al.

Apr 7, 2025

Neurology

Acute Ischemic Stroke Management

article

Analysis of 20,000 Patients with Acute Ischemic Stroke treated with Endovascular Thrombectomy using Machine Learning Clustering Algorithm (P12-13.006)

Rafay A. Khan, Aryan Malhotra, Adam Kiss, Fawaz Al‐Mufti, Mateusz Faltyn

April 7, 2025article en

Abstract

This study employs machine learning-based clustering algorithms to identify unique clinical patient profiles with Acute Ischemic Stroke (AIS) treated with Endovascular Thrombectomy (EVT), providing insights into the stratification of risk factors and treatment outcomes.

NeurologyVol. 104(7_Supplement_1)

Westchester Medical Center (US), New York Medical College (US)

Openalex Percentile: Top 90%

Acute Ischemic Stroke Management

DOI Source OpenAlex

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