分析医疗聊天机器人对话中的症状模式，挖掘可诊断的文本线索。
Text Mining Analysis of Symptom Patterns in Medical Chatbot Conversations
- 用LDA、K-Means等多方法解析患者描述的症状主题与关联。
- 发现发热头痛、皮疹瘙痒等组合具有高置信度共现关系。
- 为远程医疗系统提升诊断支持与交互体验提供可扩展框架。
数字健康系统的快速发展促使人们更深入理解其如何解读和呈现患者自述症状。医疗聊天机器人被用于提供临床支持并改善用户体验，使从文本对话中提取有意义的临床模式成为可能。本研究采用多种自然语言处理方法，分析医学聊天机器人对话中的症状描述频率与模式。基于包含960条多轮对话、涵盖24种临床病症的Medical Conversations to Disease Dataset，构建了标准化的医患对话表示，用于计算分析。多方法流程包括：使用隐含狄利克雷分布（LDA）识别潜在症状主题，利用K-Means按相似性聚类症状描述，通过基于Transformer的命名实体识别（NER）提取医学概念，并运用Apriori算法发现频繁出现的症状组合。分析结果显示存在结构清晰的临床相关主题，聚类具有中等一致性，且发热-头痛、皮疹-瘙痒等症状对具有较高置信度。结果表明，对话式医疗数据可作为早期症状识别的重要信号，有助于增强决策支持并改进用户与远程医疗技术的互动。该研究展示了一种将非结构化对话转化为可行动症状知识的方法，为未来医疗聊天机器人的性能优化、可靠性提升与临床应用提供可扩展框架。
原文摘要 · Abstract (English)
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.
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