Toward automatic evaluation of medical abstracts: The current value of sentiment analysis and machine learning for classification of the importance of PubMed abstracts of randomized trials for stroke.

Journal: Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association

Published Date: Jun 23, 2020

Abstract

BACKGROUND: Text mining with automatic extraction of key features is gaining increasing importance in science and particularly medicine due to the rapidly increasing number of publications.

Authors

Igor Fischer

Division of Informatics and Statistics, Department of Neurosurgery, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany.
Hans-Jakob Steiger

Department of Neurosurgery, Medical Faculty, Heinrich-Heine-University, Moorenstraße 5, 40225, Düsseldorf, Germany. steiger@uni-duesseldorf.de.

Keywords

Abstracting and Indexing Data Mining Deep Learning Humans Machine Learning Pattern Recognition, Automated PubMed Randomized Controlled Trials as Topic

External Resources

View on PubMed Access via DOI PubMed (32807454)

Toward automatic evaluation of medical abstracts: The current value of sentiment analysis and machine learning for classification of the importance of PubMed abstracts of randomized trials for stroke.

Abstract

Authors

Keywords

External Resources

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