Dense phenotyping from electronic health records enables machine learning-based prediction of preterm birth

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Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 1741-7015

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: Dense phenotyping from electronic health records enables machine learning-based prediction of preterm birth ; volume:20 ; number:1 ; day:28 ; month:9 ; year:2022 ; pages:1-21 ; date:12.2022
BMC medicine ; 20, Heft 1 (28.9.2022), 1-21, 12.2022

Creator: Abraham, Abin
Le, Brian
Kosti, Idit
Straub, Peter
Velez-Edwards, Digna R.
Davis, Lea K.
Newton, J. M.
Muglia, Louis J.
Rokas, Antonis
Bejan, Cosmin A.
Sirota, Marina
Capra, John A.

Contributor: SpringerLink (Online service)

DOI: 10.1186/s12916-022-02522-x

URN: urn:nbn:de:101:1-2022112221211343166387

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:21 AM CEST

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Associated

Abraham, Abin
Le, Brian
Kosti, Idit
Straub, Peter
Velez-Edwards, Digna R.
Davis, Lea K.
Newton, J. M.
Muglia, Louis J.
Rokas, Antonis
Bejan, Cosmin A.
Sirota, Marina
Capra, John A.
SpringerLink (Online service)

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Dense phenotyping from electronic health records enables machine learning-based prediction of preterm birth

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