Conceptual Approach to the Use of Information Acquired in Social Media for Medial Decisions

Masuma Mammadovа 1 * , Zarifa Jabrayilova 1, Aytac Isayeva 1
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1 Institute of Information Technology of the National Academy of Sciences of Azerbaijan, Baku, AZERBAIJAN
* Corresponding Author
Online Journal of Communication and Media Technologies, Volume 10, Issue 2, Article No: e202007. https://doi.org/10.29333/ojcmt/7877
OPEN ACCESS   611 Views   390 Downloads   Published online: 03 Apr 2020
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ABSTRACT

A conceptual approach to the use of information collected in medical social media for decision-making is proposed. The formation of e-medicine has turned the medical social media environment into an important source of information for improving the medical decision-making process, taking into account public opinion. Referring to this source, the information collected to obtain the data essential for medical decision-making is classified, and the medical social media environment is segmented for user relations. The information collected in the physician-patient segment is taken as a research object, and the inquiries of e-patients in a number of national medical resources are statistically analyzed. Referring to the results and demographic data of e-patients, the activity of the stakeholders in medical social media is assessed, and the informative indicators for medical decisions are defined. The process of medical decision-making is formally described. The results of the study represent an innovative approach to the use of the results of statistical analysis of information collected in the national medical social media to improve medical decision-making. This approach constitutes the conceptual framework for a decision support system to improve the quality of health care, taking into account public opinion.

CITATION

Mammadovа, M., Jabrayilova, Z., & Isayeva, A. (2020). Conceptual Approach to the Use of Information Acquired in Social Media for Medial Decisions. Online Journal of Communication and Media Technologies, 10(2), e202007. https://doi.org/10.29333/ojcmt/7877

REFERENCES

  • Aksoy, M. E. (2018). A Qualitative Study on the Reasons for Social Media Addiction. European Journal of Educational Research, 7(4), 861-865. https://doi.org/10.12973/eu-jer.7.4.861
  • Alguliyev, R., Aliguliyev, R., & Yusifov, F. (2018). Role of Social Networks in E-government: Risks and Security Threats. Online Journal of Communication and Media Technologies, 8(4), 363-376. https://doi.org/10.12973/ojcmt/3957
  • Amit, P., Tejashree, W., & Swati R. M. (2014). Review of Online Product using Rule based and Fuzzy Logic. Smiley’s International Journal of Computing and Technology, 1, 39-44.
  • Bhaskar S. (2017). Examining physican use of social media in 2017. P.M360 The essential resource for pharma marketers. Retrieved from www.pm360online.com/examining-physician-use-of-social-media-in-2017/
  • Bollen, J., Mao, H., & Zeng X.-J. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8. https://doi.org/10.1016/j.jocs.2010.12.007
  • Bridewell, W., & Das, A. K. (2011). Social Network Analysis of Physician Interactions: The Effect of Institutional Boundaries on Breast Cancer Care. AMIA Annu Symp Proc. 152-160. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3243165/
  • Campanini, S. (2016). 24 Outstanding Statistics & Figures on How Social Media has Impacted the Health Care Industry. Mashable, Linkedin, Retrieved from www.linkedin.com/pulse/24-outstanding-statistics-figures-how-social-media-has-campanini
  • Campbell, L., Evans, Y., Pumper, M., & Moreno, M. A. (2016). Social media use by physicians: a qualitative study of the new frontier of medicine. BMC Medical Informatics and Decision Making, 16, 91. https://doi.org/10.1186/s12911-016-0327-y
  • Cesare, N., Grant, C. & Hawkins, J. B. (2017). Demographics in Social Media Data for Public Health Research: Does it matter? Bloomberg Data for Good Exchange Conference. Retrieved from https://arxiv.org/ftp/arxiv/papers/1710/1710.11048.pdf
  • Chang, H., & Choi, M. (2016). Big Data and Healthcare: Building an Augmented World. Healthcare informatics research, 22(3), 153-155. https://doi.org/10.4258/hir.2016.22.3.153
  • Dalal, M. K.& Zaveri, M. A. (2014). Opinion Mining from online user reviews using Fuzzy Linguistic Hedges. Applied Computational Intelligence and So. Computing, 2014(735942), 1-9. https://doi.org/10.1155/2014/735942
  • Efimenko, I. V., & Horoshevskij, V. F. (2016). Online consultations in the medical field: knowledge extraction and analytics. Proceedings of the 15th National Conference on Artificial Intelligence with the international participation, 33-46. Retrieved from http://www.raai.org/resurs/papers/kii-2016/cai2016vol2.pdf
  • Fogelson, N. S., Rubin, Z. A., & Ault, K. A. (2013). Beyond likes and tweets: an in-depth look at the physician social media landscape. Clinical Obstet Gynecol, 56(3), 495-508. https://doi.org/10.1097/GRF.0b013e31829e7638
  • Gallant, L. M., Irizarry, C., Boone, G., & Kreps L. G. (2011). Promoting Participatory Medicine with Social Media: New Media Applications on Hospital Websites that Enhance Health Education and e-Patients’ Voices. Journal of Participatory Medicine, 3. Retrieved from www.jopm.org/evidence/research/2011/10/31
  • Haque, Md., & Rahman T. (2014). Sentiment analysis by using fuzzy logic. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), 4(1), 33-48. Retrieved from https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-016-0327-y
  • Islam, M. S., Hasan, M. M., Wang, X., Germack, H. D. & Noor-E-Alam, M. (2018). A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining. Healthcare (Basel), 6(2), 54. https://doi.org/10.3390/healthcare6020054
  • Jingquan, L. (2013). Privacy policies for health social networking sites. Journal of the American Medical Informatics Association, 4(20), 704-707. https://doi.org/10.1136/amiajnl-2012-001500
  • Keckley, P. H. (2010). Issue Brief: Social Networks in Health Care Communication, collaboration and insights. Produced by the Deloitte Center for Health Solutions. Retrieved from http://www.healthinformationandcommunicationsystems.pbworks.com/w/file/fetch/93972338/SM%204b%20Full.pdf
  • Khokhar, A. (2017). Social networking for healthcare professionals. Indian journal of medical Science, 69(1), 63-66. https://doi.org/10.18203/issn.0019-5359.IndianJMedSci20170499
  • Krithika, R. D., & Rosiline, J. B. (2017). Dynamic and Reliable Intelligent Data Mining Technique on Social Media Drug Related Posts. IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (IEEE ICPCSI), 1788-1794.
  • Luneva, E. E., Efremov, A. A., & Banokin, P. I. (2015). Automated assessment of emotions of users of social networks based on fuzzy logic. Economics, Statistics and Informatics, 3, 249-254. https://doi.org/10.21686/2500-3925-2015-3-249-254
  • Mammadova, M. H., & Isayeva, A. M. (2018). E-health activity in social media environment. Problems of information society, 1, 52-62. https://doi.org/10.25045/jpis.v09.i1.05
  • Mammadova, M. H., & Jabrayilova, Z.G. (2019). Electronic medicine: formation and scientific-theoretical problems, Baku: “Information Technologies” publishing house, 319. Retrieved from https://ict.az/uploads/files/E-medicine-monograph-IIT-ANAS.pdf
  • Mammadova, M. H., Jabrayilova, Z. G., & Isayeva, A. M. (2019). Analysis of physician-patient relations segment of social media: opportunities and challenges. Problems information society, 2, 41-50. https://doi.org/10.25045/jpis.v10.i2.04
  • Martino, I. D., D’Apolito, R., McLawhorn, A. S., Fehring, K. A., Sculco, P. K., & Gasparini G. (2017). Social media for patients: benefits and drawbacks. Curr Rev Musculoskelet Med., 10(1), 141-14. https://doi.org/10.1007/s12178-017-9394-7
  • Mengxue, Z., Meizhuo, Z., &, Chen, G. (2019). Automatic discovery of adverse reactions through Chinese social media. Data mining and knowledge discovery, 33(4), 848-870.
  • Nadali, S., Murad, M. A. A. & Kadir, R. A. (2010). Sentiment classification of customer reviews based on fuzzy logic. Proceedings of the International Symposium on Information Technology (ITSim’ 10), 1037-1044. https://doi.org/10.1109/ITSIM.2010.5561583
  • Simsek, A., Elciyar, K., & Kizilhan, T. (2019). A Comparative Study on Social Media Addiction of High School and University Students. Contemporary Educational Technology, 10(2), 106-119. https://doi.org/10.30935/cet.554452
  • Swan, M. (2012). Crowdsourced Health Research Studies: An Important Emerging Complement to Clinical Trials in the Public Health Research Ecosystem. Journal of Medical Internet Research, 14(2), 46. https://doi.org/10.2196/jmir.1988
  • Tomar, D. & Agarwal, S. (2013). A survey on Data Mining approaches for Healthcare. Int. J. Bio-Sci. Bio-Technol., 5, 241-266. https://doi.org/10.14257/ijbsbt.2013.5.5.25
  • Tunc-Aksan, A., & Akbay, S.E. (2019). Smartphone Addiction, Fear of Missing Out, and Perceived Competence as Predictors of Social Media Addiction of Adolescents. European Journal of Educational Research, 8(2), 559-569. https://doi.org/10.12973/eu-jer.8.2.559
  • Usak, M., Kubiatko, M., Shabbir, M. S., Viktorovna Dudnik, O., Jermsittiparsert, K., & Rajabion, L. (2019). Health care service delivery based on the Internet of things: A systematic and comprehensive study. International Journal of Communication Systems, 33(2), e4179. https://doi.org/10.1002/dac.4179
  • Vo, A.-D., & Ock, C.-Y. (2012). Sentiment classification: a combination of PMI, sentiWordNet and fuzzy function. Proceedings of the 4th International Conference on Computational Collective Intelligence Technologies and Applications (ICCCI ‘12), 7654 (2) of Lecture Notes in Computer Science, 373-382. https://doi.org/10.1007/978-3-642-34707-8_38
  • Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-II. Information Sciences, 8(4), 301-357. https://doi.org/10.1016/0020-0255(75)90046-8