Leveraging Temporal Trends for Training Contextual Word Embeddings to Address Bias in Biomedical Applications: Development Study

Women have been underrepresented in clinical trials for many years. Machine-learning models trained on clinical trial abstracts may capture and amplify biases in the data. Specifically, word embeddings are models that enable representing words as vectors and are the building block of most natural la...

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Veröffentlicht in:	JMIR AI 2024-10, Vol.3, p.e49546
Hauptverfasser:	Agmon, Shunit, Singer, Uriel, Radinsky, Kira
Format:	Artikel
Sprache:	eng
Schlagworte:	Original Paper
Online-Zugang:	Volltext
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