title: Recommendations for antiarrhythmic drugs based on latent semantic analysis with fc-means clustering.
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.
authors.lastname: Juyoung Park
authors.lastname: Mingon Kang
authors.lastname: Junbeom Hur
authors.lastname: Kyungtae Kang
mesh_terms: D000889:Anti-Arrhythmia Agents / Q000627:therapeutic use*; D001145:Arrhythmias, Cardiac / Q000188:drug therapy; D016000:Cluster Analysis; D016208:Databases, Factual; D020000:Decision Support Systems, Clinical; D006801:Humans; D012660:Semantics
chemical_list: D000889:Anti-Arrhythmia Agents