基于语音识别和神经网络的Talking Health Care Bot (THCB): Medibot

Dwaipayan Bandopadhyay, Rajdeep Ghosh, Rajdeep Chatterjee, Nabanita Das, Bikash Sadhukhan
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引用次数: 1

摘要

COVID-19大流行在几个方面影响了医疗保健。由于宵禁、交通限制和居家指令,一些患者无法按时就诊,而不太紧急的手术则被推迟或取消。还有一些人因为害怕感染而不去医院。通过使用基于会话的人工智能程序,会说话的医疗机器人(THCB)在大流行期间可能很有用,它允许患者在不亲自去医院的情况下接受支持性护理。因此,THCB将彻底而迅速地将亲自护理转变为通过互联网进行患者咨询。为了给患者提供免费的初级医疗保健,并缩小人类医疗保健专业人员的供需差距,这项工作创建了一个基于人工智能和机器学习的会话机器人。这项研究提出了一种革命性的计算机程序,可以作为病人的个人虚拟医生。这个程序是精心设计的,经过彻底的训练,可以像真人一样与病人交流。该应用程序基于无服务器架构,根据患者的症状预测疾病。一个会说话的医疗聊天机器人面临着几个挑战,但用户的口音是迄今为止最具挑战性的。然后,本研究通过使用100种不同的声音和症状来评估所提出的模型,达到了77%的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Speech Recognition and Neural Networks based Talking Health Care Bot (THCB): Medibot
The COVID-19 pandemic has affected healthcare in several ways. Some patients were unable to make it to appointments due to curfews, transportation restrictions, and stay-at-home directives, while less urgent procedures were postponed or cancelled. Others steered clear of hospitals out of fear of contracting an infection. With the use of a conversational artificial intelligence-based program, the Talking Health Care Bot (THCB) could be useful during the pandemic by allowing patients to receive supportive care without physically visiting a hospital. Therefore, the THCB will drastically and quickly change in-person care to patient consultation through the internet. To give patients free primary healthcare and to narrow the supply-demand gap for human healthcare professionals, this work created a conversational bot based on artificial intelligence and machine learning. The study proposes a revolutionary computer program that serves as a patient's personal virtual doctor. The program was carefully created and thoroughly trained to communicate with patients as if they were real people. Based on a serverless architecture, this application predicts the disease based on the symptoms of the patients. A Talking Healthcare chatbot confronts several challenges, but the user's accent is by far the most challenging. This study has then evaluated the proposed model by using one hundred different voices and symptoms, achieving an accuracy rate of 77%.
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