ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2016
Automated Summarization of Publications Associated with Adverse Drug Reactions from PubMed.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Prediction of adverse drug reactions using demographic and non-clinical drug characteristics in FAERS data.Scientific reports · 2024Article
- Ontological Organization and Bioinformatic Analysis of Adverse Drug Reactions From Package Inserts: Development and Usability Study.Journal of medical Internet research · 2020Article
- Detecting Potential Adverse Drug Reactions Using a Deep Neural Network Model.Journal of medical Internet research · 2019Article
- Pharmacogenomic Approaches for Automated Medication Risk Assessment in People with Polypharmacy.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018Article
- Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure.JMIR medical informatics · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Academic literature provides rich and up-to-date information concerning adverse drug reactions (ADR), but it is time consuming and labor intensive for physicians to obtain information of ADRs from academic literature because they would have to generate queries, review retrieved articles and summarize the results. In this study, a method is developed to automatically detect and summarize ADRs from journal articles, rank them and present them to physicians in a user-friendly interface. The method studied ADRs for 6 drugs and returned on average 4.8 ADRs that were correct. The results demonstrated this method was feasible and effective. This method can be applied in clinical practice for assisting physicians to efficiently obtain information about ADRs associated with specific drugs. Automated summarization of ADR information from recent publications may facilitate translation of academic research into actionable information at point of care.
Identifiers
27570654PMC5001779What 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.