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Investigating Algorithmic Bias in Machine Learning Prediction Models of Suicide Attempts in Multiple Clinical Settings by Race/Ethnicity and Gender

2026-02-07

Abstract excerpt

<p>Importance: Machine learning models reflect the training data, and may thus learn and perpetuate healthcare disparities. <h4>Objective:</h4> To evaluate whether performance of a validated machine learning model predicting suicide attempts varies by race/ethnicity or gender from electronic health records (EHRs). <h4>Design:</h4> In this prognostic study, we re-analyzed previously validated landmark prediction mo...

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Literature Corpus work
a3a9e5fc-96b9-5598-8a2f-5b82368da844
DOI
10.31234/osf.io/yq983_v1
Open publication

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Investigating Algorithmic Bias in Machine Learning Prediction Models of Suicide Attempts in Multiple Clinical Settings by Race/Ethnicity and GenderDOI 10.31234/osf.io/yq983_v1
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