Data‐driven discovery of probable Alzheimer's disease and related dementia subphenotypes using elect...
Data‐driven discovery of probable Alzheimer's disease and related dementia subphenotypes using electronic health records
About this item
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Author / Creator
Publisher
United States: John Wiley & Sons, Inc
Journal title
Language
English
Formats
Publication information
Publisher
United States: John Wiley & Sons, Inc
Subjects
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Scope and Contents
Contents
Introduction
We sought to assess longitudinal electronic health records (EHRs) using machine learning (ML) methods to computationally derive probable Alzheimer's Disease (AD) and related dementia subphenotypes.
Methods
A retrospective analysis of EHR data from a cohort of 7587 patients seen at a large, multi‐specialty urban academic medica...
Alternative Titles
Full title
Data‐driven discovery of probable Alzheimer's disease and related dementia subphenotypes using electronic health records
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_c452e72dafdf4770a8af878988acf662
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_c452e72dafdf4770a8af878988acf662
Other Identifiers
ISSN
2379-6146
E-ISSN
2379-6146
DOI
10.1002/lrh2.10246
