Article

A computable phenotype for patients with SARS-CoV2 testing that occurred outside the hospital

2023-01-19

Abstract excerpt

<h4>Objective</h4> To identify a cohort of COVID-19 cases, including when evidence of virus positivity was only mentioned in the clinical text, not in structured laboratory data in the electronic health record (EHR). <h4>Materials and Methods</h4> Statistical classifiers were trained on feature representations derived from unstructured text in patient electronic health records (EHRs). We used a proxy dataset of pa...

Topics

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

Identifiers and source

Literature Corpus work: 798d80ef-04cc-507d-8663-9033f1a09224
DOI: 10.1101/2023.01.19.23284738

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

A computable phenotype for patients with SARS-CoV2 testing that occurred outside the hospitalDOI 10.1101/2023.01.19.23284738Read

Accuracy of Computable Phenotyping Approaches for SARS-CoV-2 Infection and COVID-19 Hospitalizations from the Electronic Health RecordSemantic proximity Read

Distinguishing Admissions Specifically for COVID-19 from Incidental SARS-CoV-2 Admissions: A National EHR Research Consortium StudySemantic proximity Read

Simple risk scores to predict hospitalization or death in outpatients with COVID-19 including the Omicron variantSemantic proximity Read

Analysis and Prediction of COVID-19 Patients’ False Negative Results for SARS-CoV-2 Detection with Pharyngeal Swab Specimen: A Retrospective StudySemantic proximity Read

Validation of Real-World Case Definitions for COVID-19 Diagnosis and Severe COVID-19 Illness Among Patients Infected with SARS-CoV-2: Translation of Clinical Trial Definitions to Real-World SettingsSemantic proximity Read

The Digital Analytic Patient Reviewer (DAPR) for COVID-19 Data Mart ValidationSemantic proximity Read

Low-cost algorithms for clinical notes phenotype classification to enhance epidemiological surveillance: A case study.Semantic proximity Read

Evaluating biomedical feature fusion on machine learning’s predictability and interpretability of COVID-19 severity typesSemantic proximity Read

Artificial intelligence driven assessment of routinely collected healthcare data is an effective screening test for COVID-19 in patients presenting to hospitalSemantic proximity Read

Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: a Feasibility StudySemantic proximity Read

Characterization of Long Covid Temporal Sub-Phenotypes by Distributed Learning from Electronic Health Record DataSemantic proximity Read

Reengineering a machine learning phenotype to adapt to the changing COVID-19 landscape: A study from the N3C and RECOVER consortiaSemantic proximity Read