Design and Implementation of Medical QA System using Machine Learning Techniques

Authors

  • Nisha Patil  Department of Computer Science and Engineering, KLS GIT, Belagavi, Karnataka, India
  • Dr. Kuldeep Sambrekar  Department of Computer Science and Engineering, KLS GIT, Belagavi, Karnataka, India

DOI:

https://doi.org/10.32628/CSEIT217635

Keywords:

Question Answering, Natural Language Processing, Semantic Search, Medical Informatics, Machine Learning, Neural Networks, Artificial Intelligence, Language Processing.

Abstract

Nowadays, people rely on Traditional books or Google for every answer to their questions on a day-to-day basis, from basic information to medical queries. Till now, people are facing problems and are unable to find the accurate answer to their questions or fetch relevant results. Also, this technique is time-consuming as people have to go through many books to obtain one relevant answer or search various websites, which is a tedious task and not an efficient way in today's world where time is the top priority, yet the majority of people follow these techniques. So, to overcome this technique and solve the current problems, we have implemented a new technique in this paper. The BERT model, pre-trains deep bidirectional representations from the unlabeled text which conditions on both left and right, as a result, provides accurate answers to the user’s query when compared to the state-of-the-art model. This same model can be further implemented in other domains to obtain accurate results.

References

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-12-30

Issue

Volume 7, Issue 6, November-December-2021

Section

Research Articles

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How to Cite

[1]
Nisha Patil, Dr. Kuldeep Sambrekar, " Design and Implementation of Medical QA System using Machine Learning Techniques" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 6, pp.129-134, November-December-2021. Available at doi : https://doi.org/10.32628/CSEIT217635