IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Dr. Query: A Predictive Mobile-Based Healthcare Tool for Querying Drug

Dr. Query: A Predictive Mobile-Based Healthcare Tool for Querying Drug
View Sample PDF
Author(s): Megha Rathi (Jaypee Institute of Information Technology, Noida, India), Vaibhav Grover (Jaypee Institute of Information Technology, Noida, India) and Twinkle Kheterpal (Jaypee Institute of Information Technology, Noida, India)
Copyright: 2020
Volume: 11
Issue: 1
Pages: 21
Source title: International Journal of Swarm Intelligence Research (IJSIR)
Editor(s)-in-Chief: Yuhui Shi (Southern University of Science and Technology (SUSTech), China)
DOI: 10.4018/IJSIR.2020010103

Purchase

View Dr. Query: A Predictive Mobile-Based Healthcare Tool for Querying Drug on the publisher's website for pricing and purchasing information.

Abstract

Drugs can help us to treat disease, but sometimes medication can cause severe side effects. With a little knowledge, one can have drugs that are intended to prevent or avoid adverse outcome. Recognizing potential drugs enhances the quality of the healthcare system and reduces the risk associated with drug intake. Several factors like drug-drug interactions and side effects should be known to us before we intake drugs. So, the authors' motive is to develop a predictive mobile-based healthcare tool that would help drug consumers to find drugs which suit them best. As an outcome, the tool will provide the names of the top 10 medicines that will be best for specified indications and do not cause specified side effects and do not or least interact with mentioned drugs. Proposed mobile-based drug query tool will provide exact query matching drugs as well as close matches by leveraging machine learning in the tool.

Related Content

Rohit Kumar Sachan, Dharmender Singh Kushwaha. © 2020. 23 pages.
Mukta Goyal, Rajalakshmi Krishnamurthi, Gokul Gupta, Abhishek Sharma. © 2020. 20 pages.
Megha Rathi, Vaibhav Grover, Twinkle Kheterpal. © 2020. 21 pages.
Audu Musa Mabu, Rajesh Prasad, Raghav Yadav. © 2020. 22 pages.
Kadda Benyahia, Meftah Mustapha, Latreche Abdelkrim. © 2020. 13 pages.
Yueming Niu, Yulin Yao. © 2020. 18 pages.
Elshrif Ibrahim Elmurngi, Abdelouahed Gherbi. © 2020. 31 pages.
Body Bottom