Dense phenotyping from electronic health records enables machine learning-based prediction of preterm birth
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
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1741-7015
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Anmerkungen
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online resource.
- Erschienen in
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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
- Urheber
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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.
- Beteiligte Personen und Organisationen
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SpringerLink (Online service)
- DOI
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10.1186/s12916-022-02522-x
- URN
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urn:nbn:de:101:1-2022112221211343166387
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:21 MESZ
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Beteiligte
- 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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