A Comparison of Citation Metrics to Machine Learning Filters for the Identification of High Quality MEDLINE Documents | Zendy

Yindalon Aphinyanaphongs | Zendy; Alexander Statnikov | Zendy; Constantin Aliferis | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

A Comparison of Citation Metrics to Machine Learning Filters for the Identification of High Quality MEDLINE Documents

Author(s) -

Yindalon Aphinyanaphongs,

Alexander Statnikov,

Constantin Aliferis

Publication year - 2006

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1197/jamia.m2031

Subject(s) - citation , gold standard (test) , computer science , machine learning , artificial intelligence , task (project management) , support vector machine , filter (signal processing) , information retrieval , statistics , mathematics , world wide web , engineering , computer vision , systems engineering

The present study explores the discriminatory performance of existing and novel gold-standard-specific machine learning (GSS-ML) focused filter models (i.e., models built specifically for a retrieval task and a gold standard against which they are evaluated) and compares their performance to citation count and impact factors, and non-specific machine learning (NS-ML) models (i.e., models built for a different task and/or different gold standard).

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research