Article

Comparative analysis, applications, and interpretation of electronic health record-based stroke phenotyping methods

2020-11-25

Abstract excerpt

<title>Abstract</title> <p><bold>Background:</bold> Accurate identification of acute ischemic stroke (AIS) patient cohorts is essential for a wide range of clinical investigations. Automated phenotyping methods that leverage electronic health records (EHRs) represent a fundamentally new approach cohort identification without current laborious and ungeneralizable generation of phenotyping algorithms. We systematic...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 3353bf46-6a2d-5f4b-a1d4-711427ef435e
DOI: 10.21203/rs.3.rs-16812/v4

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Comparative analysis, applications, and interpretation of electronic health record-based stroke phenotyping methodsDOI 10.21203/rs.3.rs-16812/v4Read

Comparative analysis, applications, and interpretation of electronic health record-based stroke phenotyping methodsSemantic proximity Read

Towards phenotyping stroke: Leveraging data from a large-scale epidemiological study to detect stroke diagnosis.Semantic proximity Read

Automated Electronic Phenotyping of Cardioembolic Stroke.Semantic proximity Read

The epidemiological characteristics of stroke phenotypes defined with ICD-10 and free-text: a cohort study linked to electronic health recordsSemantic proximity Read

Medical data and machine learning improve power of stroke genome-wide association studiesSemantic proximity Read

Prognosis of stroke subtypes in whole population health systems data: a matched cohort studySemantic proximity Read

Physician-confirmed and administrative definitions of stroke in UK Biobank reflect the same underlying genetic traitSemantic proximity Read

AI Models Powered by Emergency Medical Services Data Enhance Stroke Triage in Prehospital SettingsSemantic proximity Read

Machine Learning Based Reanalysis of Clinical Scores for Distinguishing Between Ischemic and Hemorrhagic Stroke in Low Resource SettingSemantic proximity Read

Using a k-means clustering to identify novel phenotypes of acute ischemic stroke and development of its Clinlabomics modelsSemantic proximity Read

Blood RNA Signatures Enable Accurate Discrimination of Stroke Subtype and Onset Time at Hospital AdmissionSemantic proximity Read

AI-based Prediction of Imminent Primary Stroke on Claims Data Enables Accurate Patient StratificationSemantic proximity Read