ArticleNature computational science2021
A Poisson binomial-based statistical testing framework for comorbidity discovery across electronic health record datasets.
Article in Nature computational science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- Constructing the first comorbidity networks in companion dogs in the Dog Aging Project.PLoS computational biology · 2025Article
- Constructing the first comorbidity networks in companion dogs in the Dog Aging Project.bioRxiv : the preprint server for biology · 2025Article
- Explainable artificial intelligence for stroke risk stratification in atrial fibrillation.European heart journal. Digital health · 2025Review
- An Explainable Artificial Intelligence Approach for Discovering Social Determinants of Health and Risk Interactions for Stroke in Patients With Atrial Fibrillation.The American journal of cardiology · 2023Article
- American Heart Association's Children's Strategically Focused Research Network Experience.Journal of the American Heart Association · 2023Article
- An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records.PLOS digital health · 2022Article
- A Poisson binomial-based statistical testing framework for comorbidity discovery across electronic health record datasets.Nature computational science · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Discovering the concomitant occurrence of distinct medical conditions in a patient, also known as comorbidities, is a prerequisite for creating patient outcome prediction tools. Current comorbidity discovery applications are designed for small datasets and use stratification to control for confounding variables such as age, sex or ancestry. Stratification lowers false positive rates, but reduces power, as the size of the study cohort is decreased. Here we describe a Poisson binomial-based approach to comorbidity discovery (PBC) designed for big-data applications that circumvents the need for stratification. PBC adjusts for confounding demographic variables on a per-patient basis and models temporal relationships. We benchmark PBC using two datasets to compute comorbidity statistics on 4,623,841 pairs of potentially comorbid medical terms. The results of this computation are provided as a searchable web resource. Compared with current methods, the PBC approach reduces false positive associations while retaining statistical power to discover true comorbidities.
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.