Development and validation of an interpretable neural network for prediction of postoperative in-hospital mortality

Abstract While deep neural networks (DNNs) and other machine learning models often have higher accuracy than simpler models like logistic regression (LR), they are often considered to be “black box” models and this lack of interpretability and transparency is considered a challenge for clinical adop...

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Auteurs principaux: Christine K. Lee, Muntaha Samad, Ira Hofer, Maxime Cannesson, Pierre Baldi
Format: article
Langue:EN
Publié: Nature Portfolio 2021
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Accès en ligne:https://doaj.org/article/62aa884547394eaa8877a5d5995160f4
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