基于人工智能的生成知识图谱,用于说明和开发移动医疗自我管理内容。

IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES
Frontiers in digital health Pub Date : 2024-10-07 eCollection Date: 2024-01-01 DOI:10.3389/fdgth.2024.1466211
Marc Blanchard, Vincenzo Venerito, Pedro Ming Azevedo, Thomas Hügle
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引用次数: 0

摘要

背景:以移动医疗(mHealth)自我管理项目为形式的数字疗法(DTx)在减少各种疾病(包括纤维肌痛和关节炎)的疾病活动方面表现出了有效性。目的:本研究旨在以纤维肌痛自我管理项目为例,采用基于生成式人工智能(AI)的知识图谱和网络分析,对移动医疗内容进行分类和结构化:方法:针对纤维肌痛和病毒后纤维肌痛样综合征开发了一个多模式移动医疗在线自我管理程序。除一般内容外,该程序还针对特定功能和数字角色进行了定制,这些数字角色是通过对202名接受多模态评估的慢性肌肉骨骼疼痛综合征患者进行分层聚类而确定的。文本文件包含 22,150 个单词,分为 24 个模块作为输入数据。两个生成式人工智能网络应用程序 ChatGPT-4 (OpenAI) 和 Infranodus (Nodus Labs) 被用来创建知识图谱和进行文本网络分析,包括三维可视化。对 129 个患者反馈条目进行了情感分析:ChatGPT 生成的知识图谱模型提供了一个简单的可视化概览,其中有五条主要边缘:"心理健康挑战"、"压力及其影响"、"免疫系统功能"、"Long COVID 和纤维肌痛 "以及 "疼痛管理和治疗方法"。三维可视化提供了一个更为复杂的知识图谱,"疼痛 "一词作为中心边缘出现,与 "睡眠"、"身体 "和 "压力 "紧密相连。专题聚类分析确定了 "慢性疼痛管理"、"睡眠卫生"、"免疫系统功能"、"认知疗法"、"健康饮食"、"情绪发展"、"纤维肌痛的原因 "和 "深度放松 "等类别。差距分析强调了缺失的环节,如 "消极行为 "与 "系统性炎症 "之间的联系。对自我管理计划的逆向工程显示,知识图谱与原始文本分析之间存在显著的概念相似性。对自由文本患者评论的情感分析表明,除社会接触外,大多数相关主题都是在线程序所涉及的:结论:用于文本网络分析的人工智能生成工具可以有效地构建和说明 DTx 内容。知识图谱对于提高自我管理计划的透明度、开发新的概念框架和纳入反馈回路都很有价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generative AI-based knowledge graphs for the illustration and development of mHealth self-management content.

Background: Digital therapeutics (DTx) in the form of mobile health (mHealth) self-management programs have demonstrated effectiveness in reducing disease activity across various diseases, including fibromyalgia and arthritis. However, the content of online self-management programs varies widely, making them difficult to compare.

Aim: This study aims to employ generative artificial intelligence (AI)-based knowledge graphs and network analysis to categorize and structure mHealth content at the example of a fibromyalgia self-management program.

Methods: A multimodal mHealth online self-management program targeting fibromyalgia and post-viral fibromyalgia-like syndromes was developed. In addition to general content, the program was customized to address specific features and digital personas identified through hierarchical agglomerative clustering applied to a cohort of 202 patients with chronic musculoskeletal pain syndromes undergoing multimodal assessment. Text files consisting of 22,150 words divided into 24 modules were used as the input data. Two generative AI web applications, ChatGPT-4 (OpenAI) and Infranodus (Nodus Labs), were used to create knowledge graphs and perform text network analysis, including 3D visualization. A sentiment analysis of 129 patient feedback entries was performed.

Results: The ChatGPT-generated knowledge graph model provided a simple visual overview with five primary edges: "Mental health challenges", "Stress and its impact", "Immune system function", "Long COVID and fibromyalgia" and "Pain management and therapeutic approaches". The 3D visualization provided a more complex knowledge graph, with the term "pain" appearing as the central edge, closely connecting with "sleep", "body", and "stress". Topical cluster analysis identified categories such as "chronic pain management", "sleep hygiene", "immune system function", "cognitive therapy", "healthy eating", "emotional development", "fibromyalgia causes", and "deep relaxation". Gap analysis highlighted missing links, such as between "negative behavior" and "systemic inflammation". Retro-engineering of the self-management program showed significant conceptual similarities between the knowledge graph and the original text analysis. Sentiment analysis of free text patient comments revealed that most relevant topics were addressed by the online program, with the exception of social contacts.

Conclusion: Generative AI tools for text network analysis can effectively structure and illustrate DTx content. Knowledge graphs are valuable for increasing the transparency of self-management programs, developing new conceptual frameworks, and incorporating feedback loops.

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