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Recommendations for antiarrhythmic drugs based on latent semantic analysis with fc-means clustering
- Source :
- EMBC
- Publication Year :
- 2016
- Publisher :
- IEEE, 2016.
-
Abstract
- In this paper, we propose a novel model for the appropriate recommendation of antiarrhythmic drugs by introducing a fusion of a latent semantic analysis and k-means clustering. Our model not only captures the latent factors between the types of arrhythmia and patients but also has the ability to search a group of patients with similar arrhythmias. The performance studies conducted against the MIT-BIH arrhythmia database show that clinicians accepted 66.67% of the drugs recommended from our model with a balanced f-score of 38.08%. Comparative study with previous approach also confirms the effectiveness of our model.
- Subjects :
- 030505 public health
Databases, Factual
business.industry
Latent semantic analysis
0206 medical engineering
Arrhythmias, Cardiac
02 engineering and technology
Decision Support Systems, Clinical
Machine learning
computer.software_genre
Semantics
020601 biomedical engineering
03 medical and health sciences
Heart beat
Cluster Analysis
Humans
Medicine
Data mining
Artificial intelligence
0305 other medical science
business
Cluster analysis
Anti-Arrhythmia Agents
computer
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- Accession number :
- edsair.doi.dedup.....29beaac003068fb5d30024d25d004ae1