Gender-sensitive word embeddings for healthcare.

Journal: Journal of the American Medical Informatics Association : JAMIA

Published Date: Jan 29, 2022

Abstract

OBJECTIVE: To analyze gender bias in clinical trials, to design an algorithm that mitigates the effects of biases of gender representation on natural-language (NLP) systems trained on text drawn from clinical trials, and to evaluate its performance.

Authors

Shunit Agmon

Computer Science Faculty, Technion - Israel Institute of Technology, Haifa, Israel.
Plia Gillis

Diagnostic Robotics Inc., Ariel University, Aviv, Israel.
Eric Horvitz

Microsoft.
Kira Radinsky

Department of Computer Science , Technion - Israel Institute of Technology , Haifa 3200003 , Israel.

Keywords

Clinical Trials as Topic Delivery of Health Care Female Humans Male Natural Language Processing PubMed Sexism

External Resources

View on PubMed Access via DOI PubMed (34918101)

Gender-sensitive word embeddings for healthcare.

Abstract

Authors

Keywords

External Resources

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