A machine-learning algorithm using claims data to identify patients with homozygous familial hypercholesterolemia

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: 1 Online-Ressource.

Language: Englisch

Bibliographic citation: A machine-learning algorithm using claims data to identify patients with homozygous familial hypercholesterolemia ; volume:14 ; number:1 ; day:17 ; month:4 ; year:2024 ; pages:1-8 ; date:12.2024
Scientific reports ; 14, Heft 1 (17.4.2024), 1-8, 12.2024

Creator: Gu, Jing
Epland, Matthew
Ma, Xinshuo
Park, Jina
Sanchez, Robert J.
Li, Ying

Contributor: SpringerLink (Online service)

DOI: 10.1038/s41598-024-58719-y

URN: urn:nbn:de:101:1-2406292110368.501377373207

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

Last update: 14.08.2025, 10:53 AM CEST

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Gu, Jing
Epland, Matthew
Ma, Xinshuo
Park, Jina
Sanchez, Robert J.
Li, Ying
SpringerLink (Online service)

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A machine-learning algorithm using claims data to identify patients with homozygous familial hypercholesterolemia

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Other Objects (12)

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Sex Differences in Familial Hypercholesterolemia

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Is Liver Transplant Curative in Homozygous Familial Hypercholesterolemia? A Review of Nine Global Cases

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Sex Differences in Familial Hypercholesterolemia

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The Clinical Importance of Differentiating Monogenic Familial Hypercholesterolemia from Polygenic Hypercholesterolemia

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Recognition and Treatment of Homozygous Familial Hypercholesterolemia by Primary Care Physicians: a Survey from the National Lipid Association

Metabolic systems approaches update molecular insights of clinical phenotypes and cardiovascular risk in patients with homozygous familial hypercholesterolemia

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