Biomedical Informatics Group, Departamento de Lenguajes Sistemas Informáticos e Ingeniería de Software & Departamento de Inteligencia Artificial, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, 28660 Madrid, Spain.
Faculty of Medicine, Universidad Complutense de Madrid, 28040 Madrid, Spain.
Int J Environ Res Public Health. 2018 Dec 9;15(12):2787. doi: 10.3390/ijerph15122787.
The Internet and social media is an enormous source of information. Health social networks and online collaborative environments enable users to create shared content that afterwards can be discussed. The aim of this paper is to present a novel methodology designed for quantifying relevant information provided by different participants in clinical online discussions. The main goal of the methodology is to facilitate the comparison of participant interactions in clinical conversations. A set of key indicators for different aspects of clinical conversations and specific clinical contributions within a discussion have been defined. Particularly, three new indicators have been proposed to make use of biomedical knowledge extraction based on standard terminologies and ontologies. These indicators allow measuring the relevance of information of each participant of the clinical conversation. Proposed indicators have been applied to one discussion extracted from PatientsLikeMe, as well as to two real clinical cases from the Sanar collaborative discussion system. Results obtained from indicators in the tested cases have been compared with clinical expert opinions to check indicators validity. The methodology has been successfully used for describing participant interactions in real clinical cases belonging to a collaborative clinical case discussion tool and from a conversation from a health social network. This work can be applied to assess collaborative diagnoses, discussions among patients, and the participation of students in clinical case discussions. It permits moderators and educators to obtain a quantitatively measure of the contribution of each participant.
互联网和社交媒体是一个巨大的信息来源。健康社交网络和在线协作环境使用户能够创建共享内容，然后可以对其进行讨论。本文旨在提出一种新的方法，用于量化临床在线讨论中不同参与者提供的相关信息。该方法的主要目的是促进临床对话中参与者交互的比较。已经定义了用于临床对话不同方面和特定临床贡献的一组关键指标。特别是，提出了三个新指标，以利用基于标准术语和本体的生物医学知识提取。这些指标允许衡量临床对话中每个参与者的信息相关性。所提出的指标已应用于从 PatientsLikeMe 提取的一个讨论中，以及 Sanar 协作讨论系统中的两个真实临床病例中。从测试案例中的指标获得的结果与临床专家意见进行了比较，以检查指标的有效性。该方法已成功用于描述协作临床案例讨论工具中的真实临床案例以及健康社交网络中的对话中参与者的交互。这项工作可用于评估协作诊断、患者之间的讨论以及学生参与临床案例讨论的情况。它允许主持人和教育工作者定量衡量每个参与者的贡献。
