Development of Medical Imaging Data Standardization for Imaging-Based Observational Research: OMOP Common Data Model Extension.
The rapid growth of artificial intelligence (AI) and deep learning techniques require access to large inter-institutional cohorts of data to enable the development of robust models, e.g., targeting the identification of disease biomarkers and quantifying disease progression and treatment efficacy. T...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 2; pp. 899 - 909 |
|---|---|
| Autores principales: | , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2024
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177626021&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177626021 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2024 vid: 37 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 177626021 177626021 177626021 10.1007/s10278-024-00982-6 177626021 ppf: 899 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Development of Medical Imaging Data Standardization for Imaging-Based Observational Research: OMOP Common Data Model Extension. aug: au: Park, Woo Yeon Jeon, Kyulee Schmidt, Teri Sippel Kondylakis, Haridimos Alkasab, Tarik Dewey, Blake E. You, Seng Chan Nagy, Paul affil: https://ror.org/00za53h95 Biomedical Informatics and Data Science, Johns Hopkins University, 855 N Wolfe St, Rangos 616, Baltimore, MD, USA sug: subj: Diagnostic Imaging Common Data Elements Data Collection Standards Data Analysis Standards Informatics Nonexperimental Studies Artificial Intelligence Deep Learning Semantics Vocabulary Outcome Assessment Disease Progression Treatment Outcomes Evaluation Diagnostic Reference Levels Information Resources Biological Markers Phenotype Theory Construction ab: The rapid growth of artificial intelligence (AI) and deep learning techniques require access to large inter-institutional cohorts of data to enable the development of robust models, e.g., targeting the identification of disease biomarkers and quantifying disease progression and treatment efficacy. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) has been designed to accommodate a harmonized representation of observational healthcare data. This study proposes the Medical Imaging CDM (MI-CDM) extension, adding two new tables and two vocabularies to the OMOP CDM to address the structural and semantic requirements to support imaging research. The tables provide the capabilities of linking DICOM data sources as well as tracking the provenance of imaging features derived from those images. The implementation of the extension enables phenotype definitions using imaging features and expanding standardized computable imaging biomarkers. This proposal offers a comprehensive and unified approach for conducting imaging research and outcome studies utilizing imaging features. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|