ArticleJournal of healthcare engineering2018
Improvement of Adequate Digoxin Dosage: An Application of Machine Learning Approach.
Article in Journal of healthcare engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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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
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- Artificial intelligence and computational methods in the Asia-Pacific pharmacovigilance landscape: a systematic review.International journal of public health · 2026Pooled it
- Artificial Intelligence for Drug Safety Across the Lifecycle and Decision Type: A Scoping Review.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Role of Artificial Intelligence in Nanomedicine and Organ-specific Therapy: An Updated Review.Current drug targets · 2025Review
- The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment.Reviews in cardiovascular medicine · 2024Review
- Artificial intelligence in the field of pharmacy practice: A literature review.Exploratory research in clinical and social pharmacy · 2023Review
- A Real-World Exploration into Clinical Outcomes of Direct Oral Anticoagulant Dosing Regimens in Morbidly Obese Patients Using Data-Driven Approaches.American journal of cardiovascular drugs : drugs, devices, and other interventions · 2023Observational
- Predicting Therapeutic Response to Unfractionated Heparin Therapy: Machine Learning Approach.Interactive journal of medical research · 2022Article
- Construction of a microenvironment immune gene model for predicting the prognosis of endometrial cancer.BMC cancer · 2021Article
- A four-genes based diagnostic signature for osteoarthritis.Rheumatology international · 2021Article
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery.Molecular diversity · 2021Review
- Detecting Digoxin Toxicity by Artificial Intelligence-Assisted Electrocardiography.International journal of environmental research and public health · 2021Article
- Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review.JMIR medical informatics · 2020Review
- Precision Dosing Priority Criteria: Drug, Disease, and Patient Population Variables.Frontiers in pharmacology · 2020Review
- Predicting the serum digoxin concentrations of infants in the neonatal intensive care unit through an artificial neural network.BMC pediatrics · 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 at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digoxin is a high-alert medication because of its narrow therapeutic range and high drug-to-drug interactions (DDIs). Approximately 50% of digoxin toxicity cases are preventable, which motivated us to improve the treatment outcomes of digoxin. The objective of this study is to apply machine learning techniques to predict the appropriateness of initial digoxin dosage. A total of 307 inpatients who had their conditions treated with digoxin between 2004 and 2013 at a medical center in Taiwan were collected in the study. Ten independent variables, including demographic information, laboratory data, and whether the patients had CHF were also noted. A patient with serum digoxin concentration being controlled at 0.5-0.9 ng/mL after his/her initial digoxin dosage was defined as having an appropriate use of digoxin; otherwise, a patient was defined as having an inappropriate use of digoxin. Weka 3.7.3, an open source machine learning software, was adopted to develop prediction models. Six machine learning techniques were considered, including decision tree (C4.5),
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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.