Gender-sensitive word embeddings for healthcare.

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.

Materials and methods

We analyze gender bias in clinical trials described by 16 772 PubMed abstracts (2008-2018). We present a method to augment word embeddings, the core building block of NLP-centric representations, by weighting abstracts by the number of women participants in the trial. We evaluate the resulting gender-sensitive embeddings performance on several clinical prediction tasks: comorbidity classification, hospital length of stay prediction, and intensive care unit (ICU) readmission prediction.

Results

For fema

SUBMITTER: Agmon S 

PROVIDER: S-EPMC8800511 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature