ArticlePloS one2011
Discovering disease associations by integrating electronic clinical data and medical literature.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
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- Big data from electronic health records for early and late translational cardiovascular research: challenges and potential.European heart journal · 2018Review
- Birth month affects lifetime disease risk: a phenome-wide method.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Contribution of Electronic Medical Records to the Management of Rare Diseases.BioMed research international · 2015Article
- Development and validation of a classification approach for extracting severity automatically from electronic health records.Journal of biomedical semantics · 2015Article
- Similarity-based modeling applied to signal detection in pharmacovigilance.CPT: pharmacometrics & systems pharmacology · 2014Article
- Association between Kawasaki disease and autism: a population-based study in Taiwan.International journal of environmental research and public health · 2014Article
- A pipeline to extract drug-adverse event pairs from multiple data sources.BMC medical informatics and decision making · 2014Article
- Data-driven discovery of seasonally linked diseases from an Electronic Health Records system.BMC bioinformatics · 2014Article
- Building the graph of medicine from millions of clinical narratives.Scientific data · 2014Article
- SN algorithm: analysis of temporal clinical data for mining periodic patterns and impending augury.Journal of clinical bioinformatics · 2013Article
- Describing the relationship between cat bites and human depression using data from an electronic health record.PloS one · 2013Article
- Network medicine: linking disorders.Human genetics · 2012Review
- Live sequence charts to model medical information.Theoretical biology & medical modelling · 2012Article
- Mining electronic health records: towards better research applications and clinical care.Nature reviews. Genetics · 2012Review
- OPIC: Ontology-driven Patient Information Capturing system for epilepsy.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Electronic health record (EHR) systems offer an exceptional opportunity for studying many diseases and their associated medical conditions within a population. The increasing number of clinical record entries that have become available electronically provides access to rich, large sets of patients' longitudinal medical information. By integrating and comparing relations found in the EHRs with those already reported in the literature, we are able to verify existing and to identify rare or novel associations. Of particular interest is the identification of rare disease co-morbidities, where the small numbers of diagnosed patients make robust statistical analysis difficult. Here, we introduce ADAMS, an Application for Discovering Disease Associations using Multiple Sources, which contains various statistical and language processing operations. We apply ADAMS to the New York-Presbyterian Hospital's EHR to combine the information from the relational diagnosis tables and textual discharge summaries with those from PubMed and Wikipedia in order to investigate the co-morbidities of the rare diseases Kaposi sarcoma, toxoplasmosis, and Kawasaki disease. In addition to finding well-known characteristics of diseases, ADAMS can identify rare or previously unreported associations. In particular, we report a statistically significant association between Kawasaki disease and diagnosis of autistic disorder.
Indexed as
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.