Acta Scientific Computer Sciences

Research Article Volume 3 Issue 10

Applying Deep Learning for Hypotheses Generation to Bridge Medicinal Types Ayurveda and Allopathy using Deep Belief Network in Word Embedding

Arockia Xavier Annie R* and Aishwarya T

Department of Computer Science and Engineering, College of Engineering, Anna University, India

*Corresponding Author: Arockia Xavier Annie R, Department of Computer Science and Engineering, College of Engineering, Anna University, India.

Received: August 04, 2021; Published: 00-00

Abstract

There is a humongous amount of information available in the biomedical field that leads to opportunities and several open issues. Biomedical research is towards the discovery of new cost efficient and side effects free drugs. Manual processing of this data could consume very high man-hours of labor. Automatic text processing of the abstracts of the research paper enhances the efficiency and could lead to the identification of interesting new hypotheses. Hence, hypotheses generation promises to shrink the number of possible substitutes that a medical researcher has to try in order to get a valid drug for a disease. Therefore, training word embedding using deep learning techniques improves the efficiency in vocabulary discovery and the classification of terms into various classes. The association between different diseases would serve as the base to derive hypotheses of drugs for the diseases. Our proposed work using Deep Belief Network (DBN) trained using the abstracts of the research papers has an accuracy of 82.98% whereas the traditional text processing method of hypotheses generation has an accuracy of 67.8%. Our work has provided symptom-disease, and disease-drug hypothesis that enables new alternatives and bridges between the Allopathy and Ayurveda drugs. It also intensifies and reduces the all possibilities in creation and testing of drugs for a particular disease. The hypothesis helps in reducing this all possibilities of drugs relevant to a disease.

 

Keywords: Disease-Drug Generation; Ayurveda and Allopathy; Deep Learning; Hypothesis Generation; Query Processing;

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Citation

Citation: Arockia Xavier Annie R and Aishwarya T. “Applying Deep Learning for Hypotheses Generation to Bridge Medicinal Types Ayurveda and Allopathy using Deep Belief Network in Word Embedding". Acta Scientific Computer Sciences 3.10 (2021): .

Copyright

Copyright: © 2021 Arockia Xavier Annie R and Aishwarya T. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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