ArticleJournal of the American Medical Informatics Association : JAMIA2018
High-fidelity phenotyping: richness and freedom from bias.
Article in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
50 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Development and validation of early warning score system: A systematic literature review.Journal of biomedical informatics · 2020Pooled it
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- Active Safety Surveillance Using Real-World Evidence (ASSURE) Implementation: Transparent and Reproducible Real-World Evidence Standardized Framework to Support Safety Signal Evaluation.Pharmacoepidemiology and drug safety · 2026Article
- Characteristics, treatment and survival of patients with chondrosarcoma in five European countries: a DARWIN EU® cohort study.Acta oncologica (Stockholm, Sweden) · 2026Article
- Fairness-aware K-means clustering in digital mental health for higher education students: a generalizable framework for equitable clustering.JAMIA open · 2026Article
- A probabilistic approach for building disease phenotypes across electronic health records.BioData mining · 2025Article
- An automation framework for clinical codelist development validated with UK data from patients with multiple long-term conditions.BMC medical research methodology · 2025Article
- CohortDiagnostics: Phenotype evaluation across a network of observational data sources using population-level characterization.PloS one · 2025Article
- The necessity of validity diagnostics when drawing causal inferences from observational data: lessons from a multi-database evaluation of the risk of non-infectious uveitis among patients exposed to RemicadeBMC medical research methodology · 2024Article
- Derivation and validation of generalized sepsis-induced acute respiratory failure phenotypes among critically ill patients: a retrospective study.Critical care (London, England) · 2024Article
- US public health surveillance, reimagined.Learning health systems · 2024Article
- Derivation and Validation of Generalized Sepsis-induced Acute Respiratory Failure Phenotypes Among Critically Ill Patients: A Retrospective Study.Research square · 2024Article
- Article
- Variation in Phenotype Definitions in Observational Clinical Research: A Review of Three Conditions.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Review
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- Phenomics and Robust Multiomics Data for Cardiovascular Disease Subtyping.Arteriosclerosis, thrombosis, and vascular biology · 2023Review
- A simple modeling framework for prediction in the human glucose-insulin system.Chaos (Woodbury, N.Y.) · 2023Article
- Article
- From real-world electronic health record data to real-world results using artificial intelligence.Annals of the rheumatic diseases · 2023Review
- Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Electronic health record phenotyping is the use of raw electronic health record data to assert characterizations about patients. Researchers have been doing it since the beginning of biomedical informatics, under different names. Phenotyping will benefit from an increasing focus on fidelity, both in the sense of increasing richness, such as measured levels, degree or severity, timing, probability, or conceptual relationships, and in the sense of reducing bias. Research agendas should shift from merely improving binary assignment to studying and improving richer representations. The field is actively researching new temporal directions and abstract representations, including deep learning. The field would benefit from research in nonlinear dynamics, in combining mechanistic models with empirical data, including data assimilation, and in topology. The health care process produces substantial bias, and studying that bias explicitly rather than treating it as merely another source of noise would facilitate addressing it.
Identifiers
What 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.