Mining Conflict Semantic from Drug Dataset for Detecting Drug Conflict

Mining Conflict Semantic from Drug Dataset for Detecting Drug Conflict

Shunxiang Zhang (Anhui University of Science and Technology, Huainan, China), Guangli Zhu (Anhui University of Science and Technology, Huainan, China), Haiyan Chen (East China University of Political Science and Law, Shanghai, China) and Dayu Yang (Anhui University of Science and Technology, Huainan, China)
DOI: 10.4018/IJCINI.2015070105
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

The detecting of drug interactions hiding in the massive drug data, especially the conflict (i.e., some drugs react with each other) detecting, plays an important role in the medical information field. This kind of conflict detecting can not only relieve the cognitive burden for doctors, but also help some people (e.g., physicians and patients etc.) avoid the risk of reactions among drugs in some extend. This paper presents a Drug Conflict Detecting (DCD) algorithm to rapidly find reactions among several drugs according to the user's query requirements. First, the user dictionary and waste words base are built according the data feature of medical data sources to effectively extract drug term including component and interaction terms. Then, all conflict semantics are mined to establish conflict knowledge base based on the results of drug term extraction. Finally, the DCD algorithm is proposed to provide rapid detection of drug conflict. The experimental results show that the proposed algorithm has high accuracy. It can effectively and rapidly implement the drug conflict detecting.
Article Preview

1. Introduction

With the rapid developing of information technology, various forms of drugs data are also developing rapidly on the Web. Vast amounts of drugs data are stored in their specific organization way. It is difficult for users to find reasonable information in the large amounts of drugs data resource according to their requirements. For example, people want to know the information of drug interactions including conflict reaction to make a reasonable choice in selecting some drugs. Actually, detecting and finding this kind of conflict reaction information is very necessary. “Capital of Chinese Medicine” magazine has investigated some prescriptions from more than 50 hospitals whose grade is above second-class in Beijing. There are obvious conflicts in the three types of prescription accounted for 94% (Liu, 2006). This investigation shows that professional people such as doctors, physicians etc., also difficultly remember a large of the conflict reaction among drugs.

Therefore, it is necessary to develop a highly intelligent system to provide conflict knowledge/semantic search and detecting. Actually, some existing works can provide theoretical and technical support for this kind of intelligent system such as Inference algebra (IA) (Wang, 2012), cognitive computing (Wang, 2012), knowledge modeling and processing tool (Wang, et. al. 2011), semantic mining (Xu, 2014) and so on. At the same time, this kind of intelligent systems has widely developed for many fields such as the system of semantic news events (Xu, 2015), online social network (Caviglione, 2014; Chen, 2014), meeting transcripts (Liu, 2011) and video organizing (Xu, 2015). Obviously, this kind of conflict search and detecting can reduce the cognitive burden of some people such as doctors, physicians and patients. This work belongs to the new development branch of cognitive informatics (Wang, 2011; Wang, et. al. 2011). At the same time, a favorable algorithm can rapidly and exactly provide the conflict information. Thus, people can select drugs healthily and rationally. However, there are so many types of drugs and so many conflict reactions exist in these drugs. How to mine the conflict semantic from the large medical data is a difficult problem.

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 11: 4 Issues (2017)
Volume 10: 4 Issues (2016)
Volume 9: 4 Issues (2015)
Volume 8: 4 Issues (2014)
Volume 7: 4 Issues (2013)
Volume 6: 4 Issues (2012)
Volume 5: 4 Issues (2011)
Volume 4: 4 Issues (2010)
Volume 3: 4 Issues (2009)
Volume 2: 4 Issues (2008)
Volume 1: 4 Issues (2007)
View Complete Journal Contents Listing