Work

Abstract WP331: Identifying Stroke Subtypes with High Accuracy Using Machine Learning and Icd-9 Claims Data

Year: 2017

Type: conference-abstract

Source: Stroke

DOI: 10.1161/str.48.suppl_1.wp331

Authors: Charles Esenwa, Jorge Luna, Benjamin Kummer, Hojjat Salmasian, Hooman Kamel, David Vawdrey, Mitchell Elkind

Institutions: Columbia University Irving Medical Center, Columbia University, Weill Cornell Medicine, Weill Cornell Medical Center

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: closed

OpenAlex ID: W2912294563

Cites:

Cited by:

Related to: 10