ArticleYearbook of medical informatics2014
EHR Big Data Deep Phenotyping. Contribution of the IMIA Genomic Medicine Working Group.
Article in Yearbook of medical informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03548779 (North Carolina Genomic Evaluation by Next-generation Exome Sequencing, 2), which is not on this map. Cited by 17 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
North Carolina Genomic Evaluation by Next-generation Exome Sequencing, 2
Who cites it
17 citing papers in PubMed.
- PheNominal: an EHR-integrated web application for structured deep phenotyping at the point of care.BMC medical informatics and decision making · 2022Article
- Assessment of structured data elements for social risk factors.The American journal of managed care · 2022Article
- Sensitivity and Specificity of Real-World Social Factor Screening Approaches.Journal of medical systems · 2021Article
- High-throughput phenotyping with temporal sequences.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: A case report.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Automated detection of altered mental status in emergency department clinical notes: a deep learning approach.BMC medical informatics and decision making · 2019Article
- Artificial Intelligence vs. Natural Stupidity: Evaluating AI readiness for the Vietnamese Medical Information System.Journal of clinical medicine · 2019Review
- Article
- Data integration strategies for predictive analytics in precision medicine.Personalized medicine · 2018Review
- Careflow Mining Techniques to Explore Type 2 Diabetes Evolution.Journal of diabetes science and technology · 2018Article
- Clinical Research Informatics for Big Data and Precision Medicine.Yearbook of medical informatics · 2016Article
- An information model for computable cancer phenotypes.BMC medical informatics and decision making · 2016Article
- Precision medicine informatics.Journal of the American Medical Informatics Association : JAMIA · 2016Article
- Interoperability between phenotypes in research and healthcare terminologies--Investigating partial mappings between HPO and SNOMED CT.Journal of biomedical semantics · 2016Article
- Big Data Technologies: New Opportunities for Diabetes Management.Journal of diabetes science and technology · 2015Review
- Big Data and Comparative Effectiveness Research in Radiation Oncology: Synergy and Accelerated Discovery.Frontiers in oncology · 2015Review
- A knowledge-based, automated method for phenotyping in the EHR using only clinical pathology reports.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2015Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
objectivesGiven the quickening speed of discovery of variant disease drivers from combined patient genotype and phenotype data, the objective is to provide methodology using big data technology to support the definition of deep phenotypes in medical records.
methodsAs the vast stores of genomic information increase with next generation sequencing, the importance of deep phenotyping increases. The growth of genomic data and adoption of Electronic Health Records (EHR) in medicine provides a unique opportunity to integrate phenotype and genotype data into medical records. The method by which collections of clinical findings and other health related data are leveraged to form meaningful phenotypes is an active area of research. Longitudinal data stored in EHRs provide a wealth of information that can be used to construct phenotypes of patients. We focus on a practical problem around data integration for deep phenotype identification within EHR data. The use of big data approaches are described that enable scalable markup of EHR events that can be used for semantic and temporal similarity analysis to support the identification of phenotype and genotype relationships.
conclusionsStead and colleagues' 2005 concept of using light standards to increase the productivity of software systems by riding on the wave of hardware/processing power is described as a harbinger for designing future healthcare systems. The big data solution, using flexible markup, provides a route to improved utilization of processing power for organizing patient records in genotype and phenotype research.
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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.