Tripathy Sushreeta, Singh Rishabh, Ray Mousim
Dept. of CA, ITER, S'O'A(DU), Bhubaneswar, India.
Dept. of CSE, ITER, S'O'A(DU), Bhubaneswar, India.
Procedia Comput Sci. 2023;218:1335-1341. doi: 10.1016/j.procs.2023.01.112. Epub 2023 Jan 31.
The world was taken aback when the Covid-19 pandemic hit in 2019. Ever since precautions have been taken to prevent the spreading or mutating of the virus, but the virus still keeps spreading and mutating. Scientists predict that the virus is going to stay for a long time but with reduced effectiveness. Recognizing the symptoms of the virus is essential in order to provide proper treatment for the virus. Visiting hospitals for consultation becomes quite difficult when people are supposed to maintain social distancing. Recently neural network generative models have shown impressive abilities in developing chatbots. However, using these neural network generative models that lack the required Covid specific knowledge to develop a Covid consulting system makes them difficult to be scaled. In order to bridge the gap between patients and a limited number of doctors we have proposed a Covid consulting agent by integrating the medical knowledge of Covid-19 with the neural network generative models. This system will automatically scan patient's dialogues seeking for a consultation to recognize the symptoms for Covid-19. The transformer and pretrained systems of BERT-GPT and GPT were fine-tuned CovidDialog-English dataset to generate responses for Covid-19 which were doctor-like and clinically meaningful to further solve the problem of the surging demand for medical consultations compared to the limited number of medical professionals. The results are evaluated and compared using multiple evaluation metrics which are NIST-, perplexity, BLEU-, METEOR, Entropy- and Dist-. In this paper, we also hope to prove that the results obtained from the automated dialogue systems were significantly similar to human evaluation. Furthermore, the evaluation shows that state-of-the-art BERT-GPT performs better.
2019年新冠疫情爆发时，全世界都为之震惊。自那以后，人们采取了各种预防措施来防止病毒传播或变异，但病毒仍在不断传播和变异。科学家预测，这种病毒将长期存在，但致病性会降低。识别病毒症状对于提供恰当的治疗至关重要。当人们需要保持社交距离时，去医院咨询变得相当困难。最近，神经网络生成模型在开发聊天机器人方面展现出了令人印象深刻的能力。然而，使用这些缺乏新冠特定知识的神经网络生成模型来开发新冠咨询系统，会使其难以扩展。为了弥合患者与数量有限的医生之间的差距，我们通过将新冠病毒的医学知识与神经网络生成模型相结合，提出了一种新冠咨询代理。该系统将自动扫描患者寻求咨询的对话，以识别新冠病毒的症状。对BERT - GPT和GPT的Transformer及预训练系统在CovidDialog - English数据集上进行微调，以生成类似医生且具有临床意义的新冠病毒相关回复，从而进一步解决与有限数量医疗专业人员相比医疗咨询需求激增的问题。使用NIST、困惑度、BLEU、METEOR、熵和Dist等多种评估指标对结果进行评估和比较。在本文中，我们还希望证明从自动对话系统获得的结果与人工评估结果显著相似。此外，评估表明，最先进的BERT - GPT表现更佳。
