ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2015
Mining Electronic Health Records using Linked Data.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Constructing knowledge graphs and their biomedical applications.Computational and structural biotechnology journal · 2020Review
- Susan G. Komen Big Data for Breast Cancer Initiative: How Patient Advocacy Organizations Can Facilitate Using Big Data to Improve Patient Outcomes.JCO precision oncology · 2019Article
- Utilizing electronic health records to predict acute kidney injury risk and outcomes: workgroup statements from the 15(th) ADQI Consensus Conference.Canadian journal of kidney health and disease · 2016Review
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
No grant is acknowledged in the PubMed record.
Abstract
Meaningful Use guidelines have pushed the United States Healthcare System to adopt electronic health record systems (EHRs) at an unprecedented rate. Hospitals and medical centers are providing access to clinical data via clinical data warehouses such as i2b2, or Stanford's STRIDE database. In order to realize the potential of using these data for translational research, clinical data warehouses must be interoperable with standardized health terminologies, biomedical ontologies, and growing networks of Linked Open Data such as Bio2RDF. Applying the principles of Linked Data, we transformed a de-identified version of the STRIDE into a semantic clinical data warehouse containing visits, labs, diagnoses, prescriptions, and annotated clinical notes. We demonstrate the utility of this system though basic cohort selection, phenotypic profiling, and identification of disease genes. This work is significant in that it demonstrates the feasibility of using semantic web technologies to directly exploit existing biomedical ontologies and Linked Open Data.
Identifiers
26306276PMC4525267What 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.