Background: The COVID-19 pandemic has limited daily activities and even contact between patients and primary care providers. This makes it more difficult to provide adequate primary care services, which include connecting patients to an appropriate medical specialist. A smartphone-compatible artificial intelligence (AI) chatbot that classifies patients’ symptoms and recommends the appropriate medical specialty could provide a valuable solution. Objective: In order to establish a contactless method of recommending the appropriate medical specialty, this study aimed to construct a deep learning–based natural language processing (NLP) pipeline and to develop an AI chatbot that can be used on a smartphone. Methods: We collected 118,008 sentences containing information on symptoms with labels (medical specialty), conducted data cleansing, and finally constructed a pipeline of 51,134 sentences for this study. Several deep learning models, including 4 different long short-term memory (LSTM) models with or without attention and with or without a pretrained FastText embedding layer, as well as bidirectional encoder representations from transformers for NLP, were trained and validated using a randomly selected test data set. The performance of the models was evaluated on the basis of the precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). An AI chatbot was also designed to make it easy for patients to use this specialty recommendation system. We used an open-source framework called “Alpha” to develop our AI chatbot. This takes the form of a web-based app with a frontend chat interface capable of conversing in text and a backend cloud-based server application to handle data collection, process the data with a deep learning model, and offer the medical specialty recommendation in a responsive web that is compatible with both desktops and smartphones. Results: The bidirectional encoder representations from transformers model yielded the best performance, with an AUC of 0.964 and F1-score of 0.768, followed by LSTM model with embedding vectors, with an AUC of 0.965 and F1-score of 0.739. Considering the limitations of computing resources and the wide availability of smartphones, the LSTM model with embedding vectors trained on our data set was adopted for our AI chatbot service. We also deployed an Alpha version of the AI chatbot to be executed on both desktops and smartphones. Conclusions: With the increasing need for telemedicine during the current COVID-19 pandemic, an AI chatbot with a deep learning–based NLP model that can recommend a medical specialty to patients through their smartphones would be exceedingly useful. This chatbot allows patients to identify the proper medical specialist in a rapid and contactless manner, based on their symptoms, thus potentially supporting both patients and primary care providers.

This is the abstract only. Read the full article on the JMIR site. JMIR is the leading open access journal for eHealth and healthcare in the Internet age.

中文翻译：

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人工智能聊天机器人在智能手机上的医学专业建议：开发和部署

背景：COVID-19大流行的日常活动有限，甚至患者与初级保健提供者之间的接触也很少。这使得提供足够的初级保健服务变得更加困难，其中包括将患者连接到适当的医学专家。一个智能手机兼容的人工智能（AI）聊天机器人可以对患者的症状进行分类并建议适当的医学专业知识，这可以提供有价值的解决方案。目的：为了建立推荐合适医学专业的非接触式方法，本研究旨在构建基于深度学习的自然语言处理（NLP）管道，并开发可在智能手机上使用的AI聊天机器人。方法：我们收集了118,008个句子，其中包含带有标签（医学专科）的症状信息，进行了数据清理，最终为该研究构建了51,134个句子的管道。使用随机选择的方法训练和验证了几种深度学习模型，包括4种不同的长期短期记忆（LSTM）模型，这些模型具有或不具有注意力，具有或不具有预训练的FastText嵌入层以及来自NLP转换器的双向编码器表示形式测试数据集。根据精度，召回率，F1得分和接收器工作特性曲线（AUC）下的面积评估模型的性能。还设计了一个AI聊天机器人，使患者可以轻松使用此专业推荐系统。我们使用了一个称为“ Alpha”的开源框架来开发我们的AI聊天机器人。这采用基于Web的应用程序的形式，该应用程序具有能够以文本形式进行对话的前端聊天界面，以及基于后端云的服务器应用程序来处理数据收集，使用深度学习模型处理数据并在与台式机和智能手机兼容的自适应Web。结果：来自变压器模型的双向编码器表示具有最佳性能，其AUC为0.964，F1-分数为0.768，其次是具有嵌入矢量的LSTM模型，其AUC为0.965，F1-分数为0.739。考虑到计算资源的局限性和智能手机的广泛可用性，我们的AI聊天机器人服务采用了在我们的数据集上训练有嵌入矢量的LSTM模型。我们还部署了AI聊天机器人的Alpha版本，可以在台式机和智能手机上执行。结论：随着当前COVID-19大流行期间对远程医疗的需求不断增加，具有基于深度学习的NLP模型的AI聊天机器人可以通过智能手机向患者推荐医学专业，这将非常有用。该聊天机器人使患者能够根据其症状以快速，无接触的方式找到合适的医学专家，从而有可能为患者和初级保健提供者提供支持。

这仅仅是抽象的。阅读JMIR网站上的全文。JMIR是互联网时代电子健康和医疗保健领域领先的开放获取期刊。