ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2013
Evaluation considerations for EHR-based phenotyping algorithms: A case study for drug-induced liver injury.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- Temporal Design Patterns for Digital Phenotype Cohort Selection in Critical Care: Systematic Literature Assessment and Qualitative Synthesis.JMIR medical informatics · 2020Article
- Natural Language Processing for the Evaluation of Methodological Standards and Best Practices of EHR-based Clinical Research.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Desiderata for computable representations of electronic health records-driven phenotype algorithms.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- 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
- A collaborative approach to developing an electronic health record phenotyping algorithm for drug-induced liver injury.Journal of the American Medical Informatics Association : JAMIA · 2013Article
- Defining a comprehensive verotype using electronic health records for personalized medicine.Journal of the American Medical Informatics Association : JAMIA · 2013Article
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Authors and funding
6 authors.
Funding
Abstract
Developing electronic health record (EHR) phenotyping algorithms involves generating queries that run across the EHR data repository. Algorithms are commonly assessed within demonstration studies. There remains, however, little emphasis on assessing the precision and accuracy of measurement methods during the evaluation process. Depending on the complexity of an algorithm, interim refinements may be required to improve measurement methods. Therefore, we develop an evaluation framework that incorporates both measurement and demonstration studies. We evaluate a baseline EHR phenotyping algorithm for drug induced liver injury (DILI) developed in collaboration with electronic Medical Records Genomics (eMERGE) network participants. We conduct a measurement study and report qualitative (i.e., perceptions of evaluation approach effectiveness) and quantitative (i.e., inter-rater reliability) measures. We also conduct a demonstration study and report qualitative (i.e., appropriateness of results) and quantitative (i.e., positive predictive value) measures. Given results from the measurement study, our evaluation approach underwent multiple changes including the addition of laboratory value visualization and an expanded review of clinical notes. Results from the demonstration study informed changes to our algorithm. For example, given the goal of eMERGE to identify patients who may have a genetic susceptibility to DILI, we excluded overdose patients.
Identifiers
24303321PMC3814479What 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.