Abstract
Social networks contain a lot of useful medical information in users’ and communities’ posts especially about adverse drug reactions. But before processing of the medical communities, it is important to be aware of their implicit features, which could affect the reliability of the information retrieved. We use the principal component centrality evaluation to reveal features of the distribution of influence of community members. Cosine similarity was used to compare vocabularies and structural indicators of communities of different types. As a result of the research, it was found that the medical communities have significant similarities with the communities of mothers of young children, so they can be used as an extension of the information database on the collection of the drug response. In addition, medical communities may have an atypical structure with several users who have high influence in a particular group, which shows that is necessary to verify the reliability of the information retrieved.
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Notes
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Food and Drug Administration Adverse Event Reporting System: https://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects.
- 2.
French Medicines Agency – Agence nationale de sécurité du médicament et des produits de santé.
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References
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech.: Theory Exp. 2008(10), P10008 (2008). https://doi.org/10.1088/1742-5468/2008/10/P10008
Bodin, Ö., Crona, B.I.: The role of social networks in natural resource governance: what relational patterns make a difference? Glob. Environ. Chang. 19(3), 366–374 (2009)
Borgatti, S.P., Mehra, A., Brass, D.J., Labianca, G.: Network analysis in the social sciences. Science 323(5916), 892–895 (2009)
Catanese, S., De Meo, P., Ferrara, E., Fiumara, G., Provetti, A.: Extraction and analysis of facebook friendship relations. In: Abraham, A. (ed.) Computational Social Networks, pp. 291–324. Springer, London (2012)
Csardi, G., Nepusz, T.: The igraph software package for complex network research. InterJournal Complex Syst. 1695(5), 1–9 (2006)
Foster, J.M., van der Molen, T., de Jong-van den Berg, L.: Patient-reporting of side effects may provide an important source of information in clinical practice. Eur. J. Clin. Pharmacol. 63(10), 979–980 (2007)
Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1978)
Ilyas, M.U., Radha, H.: Identifying influential nodes in online social networks using principal component centrality. In: 2011 IEEE International Conference on Communications (ICC), pp. 1–5. IEEE (2011). https://doi.org/10.1109/icc.2011.5963147
Jiang, L., Yang, C.C.: User recommendation in healthcare social media by assessing user similarity in heterogeneous network. Artif. Intell. Med. 81, 63–77 (2017)
Loscalzo, S., Yu, L.: Social network analysis: tasks and tools. In: Liu, H., Salerno, J.J., Young, M.J. (eds.) Social Computing, Behavioral Modeling, and Prediction, pp. 151–159. Springer, Boston (2008)
Newman, M.E.: Mixing patterns in networks. Phys. Rev. E 67(2), 026126 (2003). https://doi.org/10.1103/PhysRevE.67.026126
Rathert, C., Wyrwich, M.D., Boren, S.A.: Patient-centered care and outcomes: a systematic review of the literature. Med. Care Res. Rev. 70(4), 351–379 (2013). https://doi.org/10.1177/1077558712465774
Rykov, J.: Struktura social’nyh svjazej v virtual’nyh soobshhestvah: sravnitel’nyj analiz onlajn-grupp social’noj seti “VKontakte” (in Russian). Ph.D. thesis, Higher School of Economics - National Research University (2016)
Saleh, H.A., Fourrier-Réglat, A., Diogène, E.: Patient-centered pharmacovigilance: a review. Trop. J. Pharm. Res. 17(1), 179–188 (2018)
Sela, A., Milo-Cohen, O., Ben-Gal, I., Kagan, E.: Increasing the flow of rumors in social networks by spreading groups. arXiv preprint arXiv:1704.02095 (2017)
Sidorov, G., Gelbukh, A., Gómez-Adorno, H., Pinto, D.: Soft similarity and soft cosine measure: similarity of features in vector space model. Computación Y Sist. 18(3), 491–504 (2014)
Smith, M.Y., Benattia, I.: The patient’s voice in pharmacovigilance: pragmatic approaches to building a patient-centric drug safety organization. Drug Saf. 39(9), 779–785 (2016). https://doi.org/10.1007/s40264-016-0426-9
Valente, T.W.: Opinion leader interventions in social networks. BMJ: Br. Med. J. 333(7578), 1082–1083 (2006)
Van Grootheest, K., de Jong-van den Berg, L.: Patients’ role in reporting adverse drug reactions. Expert. Opin. Drug Saf. 3(4), 363–368 (2004)
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Lobantsev, A. et al. (2018). Specifics Analysis of Medical Communities in Social Network Services. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11314. Springer, Cham. https://doi.org/10.1007/978-3-030-03493-1_21
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