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Natural Language Processing for Covid-19 Consulting System.

Sushreeta Tripathy1, Rishabh Singh2, Mousim Ray2

  • 1Dept. of CA, ITER, S'O'A(DU), Bhubaneswar, India.

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Summary
This summary is machine-generated.

This study introduces a novel AI chatbot for COVID-19 symptom recognition and consultation, integrating medical knowledge with generative models. The system aims to alleviate healthcare burdens by providing doctor-like responses, improving access to medical advice.

Keywords:
NLPcovid-19dialogue systemsequence-to-sequencetransformer

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Area of Science:

  • Artificial Intelligence
  • Medical Informatics
  • Natural Language Processing

Background:

  • The COVID-19 pandemic necessitates continuous monitoring and consultation, yet social distancing poses challenges for in-person medical visits.
  • Existing neural network generative models lack specific COVID-19 medical knowledge, limiting their scalability for healthcare applications.
  • A significant gap exists between patient demand for consultations and the availability of medical professionals.

Purpose of the Study:

  • To develop a COVID-19 consulting agent by integrating specific medical knowledge with advanced neural network generative models.
  • To create a system capable of automatically scanning patient dialogues to recognize COVID-19 symptoms.
  • To bridge the gap between patients and limited healthcare providers by offering automated, clinically relevant consultations.

Main Methods:

  • Fine-tuning transformer and pretrained systems like BERT-GPT and GPT using the CovidDialog-English dataset.
  • Developing a COVID consulting agent that integrates COVID-19 medical knowledge with generative AI.
  • Utilizing automated dialogue systems to recognize symptoms and generate doctor-like responses.

Main Results:

  • The developed system generates clinically meaningful and doctor-like responses for COVID-19 related queries.
  • Evaluation using metrics such as NIST-n, perplexity, BLEU-n, METEOR, Entropy-n, and Dist-n demonstrates system effectiveness.
  • BERT-GPT demonstrated superior performance compared to other state-of-the-art models in generating relevant responses.

Conclusions:

  • The proposed COVID consulting agent effectively integrates medical knowledge with AI for symptom recognition and consultation.
  • Automated dialogue systems show significant similarity to human evaluations, validating their clinical utility.
  • The BERT-GPT model shows promise for enhancing AI-driven healthcare consultation systems, particularly for infectious diseases like COVID-19.