37 0

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

Title
Recommendations for Antiarrhythmic Drugs Based on Latent Semantic Analysis with K-Means Clustering
Author
강경태
Keywords
Anti-Arrhythmia Agents; Arrhythmias; Cardiac; Cluster Analysis; Databases; Factual; Decision Support Systems; Clinical; Humans; Semantics
Issue Date
2016-08
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Page. 4423-4426
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.
URI
https://ieeexplore.ieee.org/document/7591708/http://repository.hanyang.ac.kr/handle/20.500.11754/102649
ISBN
978-1-4577-0220-4
ISSN
1558-4615; 1557-170X
DOI
10.1109/EMBC.2016.7591708
Appears in Collections:
COLLEGE OF COMPUTING[E] > COMPUTER SCIENCE(소프트웨어학부) > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE