ArticleNeuroinformatics2022
pyPheWAS: A Phenome-Disease Association Tool for Electronic Medical Record Analysis.
Article in Neuroinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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
15 citing papers in PubMed, 19 citations in OpenAlex.
- PolyGenie: a reproducible Nextflow pipeline for phenome-wide association studies using polygenic risk scores.NAR genomics and bioinformatics · 2026Article
- Lifespan Pancreas Morphology for Control Versus Type 2 Diabetes Using AI on Largescale Clinical Imaging.Clinical anatomy (New York, N.Y.) · 2026Article
- HistoWAS: A Pathomics Framework for Large-Scale Feature-Wide Association Studies of Tissue Topology and Patient Outcomes.IS&T International Symposium on Electronic Imaging · 2026Article
- Deep learning-based identification of patients at increased risk of cancer using routine laboratory markers.Scientific reports · 2025Article
- Learning disentangled representations to harmonize connectome network measures.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Evaluating the impact of common clinical confounders on performance of deep-learning-based sepsis risk assessment.Frontiers in artificial intelligence · 2025Article
- PheWAS analysis on large-scale biobank data with PheTK.Bioinformatics (Oxford, England) · 2024Article
- Machine learning uncovers manganese as a key nutrient associated with reduced risk of steatotic liver disease.Liver international : official journal of the International Association for the Study of the Liver · 2024Article
- PYPE: A pipeline for phenome-wide association and Mendelian randomization in investigator-driven biobank scale analysis.Patterns (New York, N.Y.) · 2024Article
- Phenotyping Down syndrome: discovery and predictive modelling with electronic medical records.Journal of intellectual disability research : JIDR · 2024Article
- Learning site-invariant features of connectomes to harmonize complex network measures.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- pyPheWAS Explorer: a visualization tool for exploratory analysis of phenome-disease associations.JAMIA open · 2023Article
- Health Conditions Associated with Alzheimer's Disease and Vascular Dementia.Annals of neurology · 2023Article
- Batch size: go big or go home? Counterintuitive improvement in medical autoencoders with smaller batch size.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Structured deep embedding model to generate composite clinical indices from electronic health records for early detection of pancreatic cancer.Patterns (New York, N.Y.) · 2023Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Along with the increasing availability of electronic medical record (EMR) data, phenome-wide association studies (PheWAS) and phenome-disease association studies (PheDAS) have become a prominent, first-line method of analysis for uncovering the secrets of EMR. Despite this recent growth, there is a lack of approachable software tools for conducting these analyses on large-scale EMR cohorts. In this article, we introduce pyPheWAS, an open-source python package for conducting PheDAS and related analyses. This toolkit includes 1) data preparation, such as cohort censoring and age-matching; 2) traditional PheDAS analysis of ICD-9 and ICD-10 billing codes; 3) PheDAS analysis applied to a novel EMR phenotype mapping: current procedural terminology (CPT) codes; and 4) novelty analysis of significant disease-phenotype associations found through PheDAS. The pyPheWAS toolkit is approachable and comprehensive, encapsulating data prep through result visualization all within a simple command-line interface. The toolkit is designed for the ever-growing scale of available EMR data, with the ability to analyze cohorts of 100,000 + patients in less than 2 h. Through a case study of Down Syndrome and other intellectual developmental disabilities, we demonstrate the ability of pyPheWAS to discover both known and potentially novel disease-phenotype associations across different experiment designs and disease groups. The software and user documentation are available in open source at https://github.com/MASILab/pyPheWAS .
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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.