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Using Machine Learning to Develop a Contemporary Primary Graft Dysfunction Prediction Model: The International Consortium on PGD.

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Title: Using Machine Learning to Develop a Contemporary Primary Graft Dysfunction Prediction Model: The International Consortium on PGD.
Authors: Moayedi, Y.1 (AUTHOR), Foroutan, F.2 (AUTHOR), Truby, L.K.3 (AUTHOR), Han, J.4 (AUTHOR), Angleitner, P.5 (AUTHOR), Guzman, J.6 (AUTHOR), Sabatino, M.7 (AUTHOR), Felius, J.8 (AUTHOR), Zafar, H.8 (AUTHOR), Law, D.9 (AUTHOR), Van Zyl, J.8 (AUTHOR), Tremblay-Gravel, M.10 (AUTHOR), Segovia, J.11 (AUTHOR), Devore, A.3 (AUTHOR), Kim, G.4 (AUTHOR), Lasarte, M. Riva11 (AUTHOR), Knezevic, I.12 (AUTHOR), Noly, P.13 (AUTHOR), Farr, M.14 (AUTHOR), Zuckermann, A.5 (AUTHOR)
Source: Journal of Heart & Lung Transplantation. 2022Supplement, Vol. 41 Issue 4, pS11-S12. 2p.
Database: Academic Search Ultimate
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  Data: Using Machine Learning to Develop a Contemporary Primary Graft Dysfunction Prediction Model: The International Consortium on PGD.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Heart+%26+Lung+Transplantation%22">Journal of Heart & Lung Transplantation</searchLink>. 2022Supplement, Vol. 41 Issue 4, pS11-S12. 2p.
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        Value: 10.1016/j.healun.2022.01.022
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              Text: 2022Supplement
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