ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2017
Reducing the Toxicity Risk in Antibiotic Prescriptions by Combining Ontologies with a Multiple Criteria Decision Model.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017. 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
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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
3 citing papers in PubMed, 6 citations in OpenAlex.
- Applying AI to Structured Real-World Data for Pharmacovigilance Purposes: Scoping Review.Journal of medical Internet research · 2024Article
- Antibiotics: Conventional Therapy and Natural Compounds with Antibacterial Activity-A Pharmaco-Toxicological Screening.Antibiotics (Basel, Switzerland) · 2021Review
- Amplifying Domain Expertise in Clinical Data Pipelines.JMIR medical informatics · 2020Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We consider the risk of adverse drug events caused by antibiotic prescriptions. Antibiotics are the second most common cause of drug related adverse events and one of the most common classes of drugs associated with medical malpractice claims. To cope with this serious issue, physicians rely on guidelines, especially in the context of hospital prescriptions. Unfortunately such guidelines do not offer sufficient support to solve the problem of adverse events. To cope with these issues our work proposes a clinical decision support system based on expert medical knowledge, which combines semantic technologies with multiple criteria decision models. Our model links and assesses the adequacy of each treatment through the toxicity risk of side effects, in order to provide and explain to physicians a sorted list of possible antibiotics. We illustrate our approach through carefully selected case studies in collaboration with the EpiCURA Hospital Center in Belgium.
Indexed as
Identifiers
29854233PMC5977731W2784864382What 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.