医疗聊天机器人对话中的症状模式文本挖掘分析
Text Mining Analysis of Symptom Patterns in Medical Chatbot Conversations
AI总结：
本研究通过多种NLP方法分析医疗聊天机器人对话中的症状模式，揭示临床相关主题结构，提升症状解读与医疗决策支持能力。
摘要：
数字健康系统的快速成长催生了更好地理解它们如何解读和表示患者报告症状的需要。聊天机器人已被用于医疗领域，以提供临床支持并增强用户体验，从而通过聊天机器人从基于文本的数据中提供有意义的临床模式。本研究利用多种不同的自然语言处理方法来研究医学中症状描述的发生情况，并分析通过这些对话在医疗机器人中出现的模式。通过使用包含960个多轮对话并分为24种临床状况的Medical Conversations to Disease Dataset，为计算手段进一步分析创建了患者与机器人之间的标准化对话表示。多方法方法使用了多种工具，包括潜在狄利克雷分配（LDA）来识别潜在症状主题、K均值聚类按相似性分组症状描述、基于Transformer的命名实体识别（NER）提取医学概念，以及Apriori算法发现频繁症状对。分析结果表明，存在具有临床相关性的主题结构，中等程度的聚类凝聚性以及几个高置信度的症状关系，如发烧、头痛和皮疹瘙痒之间的关系。结果支持了对话医疗数据可以作为早期症状解读的有价值诊断信号，有助于加强决策支持并改善用户与远程医疗技术的互动。通过展示将无结构的自由流动对话转换为关于症状的可操作知识的方法，本工作提供了一个可扩展的框架，以进一步提高未来选择医疗聊天机器人的性能、可靠性和临床效用。
英文摘要：
The fast growth of digital health systems has led to a need to better comprehend how they interpret and represent patient-reported symptoms. Chatbots have been used in healthcare to provide clinical support and enhance the user experience, making it possible to provide meaningful clinical patterns from text-based data through chatbots. The proposed research utilises several different natural language processing methods to study the occurrences of symptom descriptions in medicine as well as analyse the patterns that emerge through these conversations within medical bots. Through the use of the Medical Conversations to Disease Dataset which contains 960 multi-turn dialogues divided into 24 Clinical Conditions, a standardised representation of conversations between patient and bot is created for further analysis by computational means. The multi-method approach uses a variety of tools, including Latent Dirichlet Allocation (LDA) to identify latent symptom themes, K-Means to group symptom descriptions by similarity, Transformer-based Named Entity Recognition (NER) to extract medical concepts, and the Apriori algorithm to discover frequent symptom pairs. Findings from the analysis indicate a coherent structure of clinically relevant topics, moderate levels of clustering cohesiveness and several high confidence rates on the relationships between symptoms like fever headache and rash itchiness. The results support the notion that conversational medical data can be a valuable diagnostic signal for early symptom interpretation, assist in strengthening decision support and improve how users interact with tele-health technology. By demonstrating a method for converting unstructured free-flowing dialogue into actionable knowledge regarding symptoms this work provides an extensible framework to further enhance future performance, dependability and clinical utility of selecting medical chatbots.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
