Measuring Relevant Information in Health Social Network Conversations and Clinical Diagnosis Cases

Albert Moreira, Raul Alonso-Calvo, Alberto Muñoz and José Crespo
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Albert Moreira: 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
Raul Alonso-Calvo: 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
Alberto Muñoz: Faculty of Medicine, Universidad Complutense de Madrid, 28040 Madrid, Spain
José Crespo: 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

IJERPH, 2018, vol. 15, issue 12, 1-17

Abstract: 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.

Keywords: social media; health; patients; healthcare professionals; collaboration measurement; medical terminologies; conversation participation indicators (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:15:y:2018:i:12:p:2787-:d:189147

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