Recommendations for Antiarrhythmic Drugs Based on Latent Semantic Analysis with k-Means Clustering

被引：0

作者：

Park, Juyoung ^{[1
]}

Kang, Mingon ^{[3
]}

Hur, Junbeom ^{[2
]}

Kang, Kyungtae ^{[1
]}

机构：

[1] Hanyang Univ, Dept Comp Sci & Engn, Ansan, South Korea

[2] Korea Univ, Dept Comp Sci & Informat Syst, Seoul, South Korea

[3] Texas A&M Univ Commerce, Dept Comp Sci & Informat Syst, Commerce, TX USA

来源：

2016 38TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2016年

基金：

新加坡国家研究基金会;

关键词：

CLASSIFICATION;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

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 kmeans 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.

引用

页码：4423 / 4426

页数：4