ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019
Comorbidity Characterization Among eMERGE Institutions: A Pilot Evaluation with the Johns Hopkins Adjusted Clinical Groups® System.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
- Estimating the economic burden of long-Covid: the additive cost of healthcare utilisation among COVID-19 recoverees in Israel.BMJ global health · 2023Article
- Review of Clinical Research Informatics.Yearbook of medical informatics · 2020Review
- Integration of NLP2FHIR Representation with Deep Learning Models for EHR Phenotyping: A Pilot Study on Obesity Datasets.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational ScienceArticle
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Authors and funding
18 authors at 7 institutions in 1 country.
Funding
Abstract
Electronic health records (EHR) are valuable to define phenotype selection algorithms used to identify cohorts ofpatients for sequencing or genome wide association studies (GWAS). To date, the electronic medical records and genomics (eMERGE) network institutions have developed and applied such algorithms to identify cohorts with associated DNA samples used to discover new genetic associations. For complex diseases, there are benefits to stratifying cohorts using comorbidities in order to identify their genetic determinants. The objective of this study was to: (a) characterize comorbidities in a range of phenotype-selected cohorts using the Johns Hopkins Adjusted Clinical Groups® (ACG®) System, (b) assess the frequency of important comorbidities in three commonly studied GWAS phenotypes, and (c) compare the comorbidity characterization of cases and controls. Our analysis demonstrates a framework to characterize comorbidities using the ACG system and identified differences in mean chronic condition count among GWAS cases and controls. Thus, we believe there is great potential to use the ACG system to characterize comorbidities among genetic cohorts selected based on EHR phenotypes.
Identifiers
31258966PMC6568092W3021417580What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.