An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as an Example

Autor: Zhiyuan Liu, Runyu Liu, Jing Yan, Zeheng Wang, Kun Lu, Liang Li, Yuanzhe Yao
Jazyk: angličtina
Rok vydání: 2019
Předmět:
FOS: Computer and information sciences
Drug-Related Side Effects and Adverse Reactions
020205 medical informatics
Article Subject
Computer Science - Artificial Intelligence
Computer science
02 engineering and technology
Traditional Chinese medicine
Ontology (information science)
lcsh:Computer applications to medicine. Medical informatics
General Biochemistry
Genetics and Molecular Biology
Pattern Recognition
Automated
03 medical and health sciences
Side effect (computer science)
Artificial Intelligence
0202 electrical engineering
electronic engineering
information engineering
Humans
Medicine
Chinese Traditional
030304 developmental biology
Structure (mathematical logic)
0303 health sciences
General Immunology and Microbiology
business.industry
Data Collection
Applied Mathematics
Reproducibility of Results
General Medicine
Artificial Intelligence (cs.AI)
Modeling and Simulation
Ontology
lcsh:R858-859.7
Neural Networks
Computer
Patient Safety
Artificial intelligence
business
Algorithms
Research Article
Drugs
Chinese Herbal
Zdroj: Computational and Mathematical Methods in Medicine, Vol 2019 (2019)
Computational and Mathematical Methods in Medicine
ISSN: 1748-6718
Popis: 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.
Databáze: OpenAIRE
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