ArticleJournal of biomedical informatics2014
Identifying and mitigating biases in EHR laboratory tests.
Article in Journal of biomedical informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 3 of them syntheses that pooled it.
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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.
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Who cites it
56 citing papers in PubMed, 3 syntheses or guidelines pooled it, 96 citations in OpenAlex.
- Enhancing Patient Outcome Prediction Through Deep Learning With Sequential Diagnosis Codes From Structured Electronic Health Record Data: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Development and validation of early warning score system: A systematic literature review.Journal of biomedical informatics · 2020Pooled it
- Progress in Biomedical Knowledge Discovery: A 25-year Retrospective.Yearbook of medical informatics · 2016Pooled it
- Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial.Nature medicine · 2025Trial
- Harmonizing HIV-1 RNA laboratory measurements in a longitudinal cohort collaboration.American journal of epidemiology · 2026Article
- Can Real-World Data From Hospital Information Systems Be Directly Utilized for Efficacy Studies: An Analysis of Data Time Points.Journal of evidence-based medicine · 2025Article
- Implicit bias in ICU electronic health record data: measurement frequencies and missing data rates of clinical variables.BMC medical informatics and decision making · 2025Article
- Multisite Pragmatic Cluster-Randomized Controlled Trial of the CONCERN Early Warning System.medRxiv : the preprint server for health sciences · 2024Article
- Evaluating Large Language Models in Extracting Cognitive Exam Dates and Scores.medRxiv : the preprint server for health sciences · 2024Article
- A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation.Journal of biomedical informatics · 2023Article
- Diagnostic suspicion bias and machine learning: Breaking the awareness deadlock for sepsis detection.PLOS digital health · 2023Article
- A simple modeling framework for prediction in the human glucose-insulin system.Chaos (Woodbury, N.Y.) · 2023Article
- Phenomics and Robust Multiomics Data for Cardiovascular Disease Subtyping.Arteriosclerosis, thrombosis, and vascular biology · 2023Review
- Building the Model.Archives of pathology & laboratory medicine · 2023Review
- Missing data matter: an empirical evaluation of the impacts of missing EHR data in comparative effectiveness research.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Ethical layering in AI-driven polygenic risk scores-New complexities, new challenges.Frontiers in genetics · 2023Article
- Standardized Description of the Feature Extraction Process to Transform Raw Data Into Meaningful Information for Enhancing Data Reuse: Consensus Study.JMIR medical informatics · 2022Article
- Inferring EHR Utilization Workflows through Audit Logs.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- On Missingness Features in Machine Learning Models for Critical Care: Observational Study.JMIR medical informatics · 2021Article
- Big Data in Nephrology.Nature reviews. Nephrology · 2021Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Electronic health record (EHR) data show promise for deriving new ways of modeling human disease states. Although EHR researchers often use numerical values of laboratory tests as features in disease models, a great deal of information is contained in the context within which a laboratory test is taken. For example, the same numerical value of a creatinine test has different interpretation for a chronic kidney disease patient and a patient with acute kidney injury. We study whether EHR research studies are subject to biased results and interpretations if laboratory measurements taken in different contexts are not explicitly separated. We show that the context of a laboratory test measurement can often be captured by the way the test is measured through time. We perform three tasks to study the properties of these temporal measurement patterns. In the first task, we confirm that laboratory test measurement patterns provide additional information to the stand-alone numerical value. The second task identifies three measurement pattern motifs across a set of 70 laboratory tests performed for over 14,000 patients. Of these, one motif exhibits properties that can lead to biased research results. In the third task, we demonstrate the potential for biased results on a specific example. We conduct an association study of lipase test values to acute pancreatitis. We observe a diluted signal when using only a lipase value threshold, whereas the full association is recovered when properly accounting for lipase measurements in different contexts (leveraging the lipase measurement patterns to separate the contexts). Aggregating EHR data without separating distinct laboratory test measurement patterns can intermix patients with different diseases, leading to the confounding of signals in large-scale EHR analyses. This paper presents a methodology for leveraging measurement frequency to identify and reduce laboratory test biases.
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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.