ArticleAnnual review of biomedical data science2018
Defining Phenotypes from Clinical Data to Drive Genomic Research.
Article in Annual review of biomedical data science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review to Summarize and Critically Appraise Existing Phenotype Libraries Using Electronic Health Records.Pharmacoepidemiology and drug safety · 2026Pooled it
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- Unsupervised Clinical Phenotyping Identifies Distinct Risk Profiles in Incisional Hernia Repair.Medicina (Kaunas, Lithuania) · 2026Article
- Deep learning-based stratification of Schizophrenia Spectrum Disorder from real-world data reveals distinct profiles of common and rare variant genetic signal.medRxiv : the preprint server for health sciences · 2026Article
- Genetic analysis of neurodegenerative diseases.The Journal of clinical investigation · 2026Review
- Multimorbidity measurement strategies for predicting hospital visits.BMC health services research · 2025Article
- SepsisJournal of medical microbiology · 2025Review
- The Difficulties of Managing Pain in People Living with Frailty: The Potential for Digital Phenotyping.Drugs & aging · 2024Article
- How much do we know about the function of mammalian genes?BMC biology · 2023Article
- Sex modifies the effect of genetic risk scores for polycystic ovary syndrome on metabolic phenotypes.PLoS genetics · 2023Article
- Impact of Pulmonary and Sleep Disorders on COVID-19 Infection Severity in a Large Clinical Biobank.medRxiv : the preprint server for health sciences · 2023Article
- Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Accessing and utilizing clinical and genomic data from an electronic health record data warehouse.Translational medicine communications · 2023Article
- Biobanking as a Tool for Genomic Research: From Allele Frequencies to Cross-Ancestry Association Studies.Journal of personalized medicine · 2022Review
- Quantifying the phenome-wide disease burden of obesity using electronic health records and genomics.Obesity (Silver Spring, Md.) · 2022Article
- Article
- Fate or coincidence: do COPD and major depression share genetic risk factors?Human molecular genetics · 2021Article
- Article
- Characterizing the Clinical and Genetic Spectrum of Polycystic Ovary Syndrome in Electronic Health Records.The Journal of clinical endocrinology and metabolism · 2021Article
- Evaluation of patient-level retrieval from electronic health record data for a cohort discovery task.JAMIA open · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The rise in available longitudinal patient information in electronic health records (EHRs) and their coupling to DNA biobanks has resulted in a dramatic increase in genomic research using EHR data for phenotypic information. EHRs have the benefit of providing a deep and broad data source of health-related phenotypes, including drug response traits, expanding the phenome available to researchers for discovery. The earliest efforts at repurposing EHR data for research involved manual chart review of limited numbers of patients but now typically involve applications of rule-based and machine learning algorithms operating on sometimes huge corpora for both genome-wide and phenome-wide approaches. We highlight here the current methods, impact, challenges, and opportunities for repurposing clinical data to define patient phenotypes for genomics discovery. Use of EHR data has proven a powerful method for elucidation of genomic influences on diseases, traits, and drug-response phenotypes and will continue to have increasing applications in large cohort studies.
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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.