ReviewJournal of medical toxicology : official journal of the American College of Medical Toxicology2018
Machine Learning to Predict, Detect, and Intervene Older Adults Vulnerable for Adverse Drug Events in the Emergency Department.
Review in Journal of medical toxicology : official journal of the American College of Medical Toxicology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Comparison of decision tree with common machine learning models for prediction of biguanide and sulfonylurea poisoning in the United States: an analysis of the National Poison Data System.BMC medical informatics and decision making · 2023Article
- The value of machine learning for prognosis prediction of diphenhydramine exposure: National analysis of 50,000 patients in the United States.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2023Article
- Factors related to substance use treatment attendance after peer recovery coach intervention in the emergency department.Drug and alcohol dependence reports · 2022Article
- Intelligent Telehealth in Pharmacovigilance: A Future Perspective.Drug safety · 2022Review
- The path from big data analytics capabilities to value in hospitals: a scoping review.BMC health services research · 2022Article
- Implementation and sustainability of a medication reconciliation toolkit: A mixed methods evaluation.American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists · 2020Observational
- JMT's Research Concepts Section: a 5-Year Evaluation.Journal of medical toxicology : official journal of the American College of Medical Toxicology · 2019Article
- A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical records.BMC medical informatics and decision making · 2019Article
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
Abstract
Adverse drug events (ADEs) are common and have serious consequences in older adults. ED visits are opportunities to identify and alter the course of such vulnerable patients. Current practice, however, is limited by inaccurate reporting of medication list, time-consuming medication reconciliation, and poor ADE assessment. This manuscript describes a novel approach to predict, detect, and intervene vulnerable older adults at risk of ADE using machine learning. Toxicologists' expertise in ADE is essential to creating the machine learning algorithm. Leveraging the existing electronic health records to better capture older adults at risk of ADE in the ED may improve their care.
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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.