ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2012
Modeling and executing electronic health records driven phenotyping algorithms using the NQF Quality Data Model and JBoss® Drools Engine.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Design and validation of a FHIR-based EHR-driven phenotyping toolbox.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Algorithmic Detection of Boolean Logic Errors in Clinical Decision Support Statements.Applied clinical informatics · 2021Article
- Toward cross-platform electronic health record-driven phenotyping using Clinical Quality Language.Learning health systems · 2020Article
- Integration of Clinical Decision Support and Electronic Clinical Quality Measurement: Domain Expert Insights and Implications for Future Direction.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Requirements and validation of a prototype learning health system for clinical diagnosis.Learning health systems · 2017Article
- A computational framework for converting textual clinical diagnostic criteria into the quality data model.Journal of biomedical informatics · 2016Article
- Developing a data element repository to support EHR-driven phenotype algorithm authoring and execution.Journal of biomedical informatics · 2016Article
- Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods.Artificial intelligence in medicine · 2016Article
- Clinical element models in the SHARPn consortium.Journal of the American Medical Informatics Association : JAMIA · 2016Article
- Developing a modular architecture for creation of rule-based clinical diagnostic criteria.BioData mining · 2016Article
- ePhenotyping for Abdominal Aortic Aneurysm in the Electronic Medical Records and Genomics (eMERGE) Network: Algorithm Development and Konstanz Information Miner Workflow.International journal of biomedical data mining · 2015Article
- Desiderata for computable representations of electronic health records-driven phenotype algorithms.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Review and evaluation of electronic health records-driven phenotype algorithm authoring tools for clinical and translational research.Journal of the American Medical Informatics Association : JAMIA · 2015Review
- A Validated Phenotyping Algorithm for Genetic Association Studies in Age-related Macular Degeneration.Scientific reports · 2015Article
- Limestone: high-throughput candidate phenotype generation via tensor factorization.Journal of biomedical informatics · 2014Article
- Scalable and High-Throughput Execution of Clinical Quality Measures from Electronic Health Records using MapReduce and the JBoss® Drools Engine.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
- Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium.Journal of the American Medical Informatics Association : JAMIA · 2013Article
- The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future.Genetics in medicine : official journal of the American College of Medical Genetics · 2013Review
- An evaluation of the NQF Quality Data Model for representing Electronic Health Record driven phenotyping algorithms.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2012Article
- Influenza detection from emergency department reports using natural language processing and Bayesian network classifiers.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With increasing adoption of electronic health records (EHRs), the need for formal representations for EHR-driven phenotyping algorithms has been recognized for some time. The recently proposed Quality Data Model from the National Quality Forum (NQF) provides an information model and a grammar that is intended to represent data collected during routine clinical care in EHRs as well as the basic logic required to represent the algorithmic criteria for phenotype definitions. The QDM is further aligned with Meaningful Use standards to ensure that the clinical data and algorithmic criteria are represented in a consistent, unambiguous and reproducible manner. However, phenotype definitions represented in QDM, while structured, cannot be executed readily on existing EHRs. Rather, human interpretation, and subsequent implementation is a required step for this process. To address this need, the current study investigates open-source JBoss® Drools rules engine for automatic translation of QDM criteria into rules for execution over EHR data. In particular, using Apache Foundation's Unstructured Information Management Architecture (UIMA) platform, we developed a translator tool for converting QDM defined phenotyping algorithm criteria into executable Drools rules scripts, and demonstrated their execution on real patient data from Mayo Clinic to identify cases for Coronary Artery Disease and Diabetes. To the best of our knowledge, this is the first study illustrating a framework and an approach for executing phenotyping criteria modeled in QDM using the Drools business rules management system.
Indexed as
Identifiers
23304325PMC3540464What 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.