ArticleComputational and mathematical methods in medicine2019
An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as an Example.
Article in Computational and mathematical methods in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Sijunzi decoction ameliorates geriatric sarcopenia by improving muscle strength, quality, and functions.Medicine · 2026Article
- A review of recent artificial intelligence for traditional medicine.Journal of traditional and complementary medicine · 2025Review
- Suboptimal capability of individual machine learning algorithms in modeling small-scale imbalanced clinical data of local hospital.PloS one · 2024Article
- Artificial Intelligence, Machine Learning, and Big Data for Ebola Virus Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2023Article
- Knowledge-Based Recurrent Neural Network for TCM Cerebral Palsy Diagnosis.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- The Use of Artificial Intelligence in Complementary and Alternative Medicine: A Systematic Scoping Review.Frontiers in pharmacology · 2022Article
- Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework.Computational intelligence and neuroscience · 2022Article
- Evaluating the Traditional Chinese Medicine (TCM) Officially Recommended in China for COVID-19 Using Ontology-Based Side-Effect Prediction Framework (OSPF) and Deep Learning.Journal of ethnopharmacology · 2021Article
- Development and Application of Artificial Intelligence in Auxiliary TCM Diagnosis.Evidence-based complementary and alternative medicine : eCAM · 2021Review
- eHealth technologies assisting in identifying potential adverse interactions with complementary and alternative medicine (CAM) or standalone CAM adverse events or side effects: a scoping review.BMC complementary medicine and therapies · 2020Article
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Authors and funding
7 authors.
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Abstract
In this work, an ontology-based model for AI-assisted medicine side-effect (SE) prediction is developed, where three main components, including the drug model, the treatment model, and the AI-assisted prediction model, of the proposed model are presented. To validate the proposed model, an ANN structure is established and trained by two hundred forty-two TCM prescriptions. These data are gathered and classified from the most famous ancient TCM book, and more than one thousand SE reports, in which two ontology-based attributions, hot and cold, are introduced to evaluate whether the prescription will cause SE or not. The results preliminarily reveal that it is a relationship between the ontology-based attributions and the corresponding predicted indicator that can be learnt by AI for predicting the SE, which suggests the proposed model has a potential in AI-assisted SE prediction. However, it should be noted that the proposed model highly depends on the sufficient clinic data, and hereby, much deeper exploration is important for enhancing the accuracy of the prediction.
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