ArticleStudies in health technology and informatics2013
Using linked data for mining drug-drug interactions in electronic health records.
Article in Studies in health technology and informatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Biomedical literature-based clinical phenotype definition discovery using large language models.Database : the journal of biological databases and curation · 2025Article
- Assessing resolvability, parsability, and consistency of RDF resources: a use case in rare diseases.Journal of biomedical semantics · 2023Article
- TP-DDI: A Two-Pathway Deep Neural Network for Drug-Drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2022Article
- Prediction of Drug-Drug Interaction Using an Attention-Based Graph Neural Network on Drug Molecular Graphs.Molecules (Basel, Switzerland) · 2022Article
- On the road to explainable AI in drug-drug interactions prediction: A systematic review.Computational and structural biotechnology journal · 2022Review
- A Review of Approaches for Predicting Drug-Drug Interactions Based on Machine Learning.Frontiers in pharmacology · 2021Review
- A computational approach to predict multi-pathway drug-drug interactions: A case study of irinotecan, a colon cancer medication.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2020Article
- DPDDI: a deep predictor for drug-drug interactions.BMC bioinformatics · 2020Article
- An Informatics-based Approach to Identify Key Pharmacological Components in Drug-Drug Interactions.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media.Briefings in bioinformatics · 2018Review
- Review
- Building and Querying RDF/OWL Database of Semantically Annotated Nuclear Medicine Images.Journal of digital imaging · 2017Article
- Leveraging Food and Drug Administration Adverse Event Reports for the Automated Monitoring of Electronic Health Records in a Pediatric Hospital.Biomedical informatics insights · 2017Article
- A Querying Method over RDF-ized Health Level Seven v2.5 Messages Using Life Science Knowledge Resources.JMIR medical informatics · 2016Article
- Mining Electronic Health Records using Linked Data.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2015Article
- Rapid learning in practice: a lung cancer survival decision support system in routine patient care data.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2014Article
- Applying MetaMap to Medline for identifying novel associations in a large clinical dataset: a feasibility analysis.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
By nature, healthcare data is highly complex and voluminous. While on one hand, it provides unprecedented opportunities to identify hidden and unknown relationships between patients and treatment outcomes, or drugs and allergic reactions for given individuals, representing and querying large network datasets poses significant technical challenges. In this research, we study the use of Semantic Web and Linked Data technologies for identifying drug-drug interaction (DDI) information from publicly available resources, and determining if such interactions were observed using real patient data. Specifically, we apply Linked Data principles and technologies for representing patient data from electronic health records (EHRs) at Mayo Clinic as Resource Description Framework (RDF), and identify potential drug-drug interactions (PDDIs) for widely prescribed cardiovascular and gastroenterology drugs. Our results from the proof-of-concept study demonstrate the potential of applying such a methodology to study patient health outcomes as well as enabling genome-guided drug therapies and treatment interventions.
Indexed as
Identifiers
23920643PMC3909652What 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.