Data Mining as an Intangible Model of Information Therapy and Seeking Behaviors in Immune Deficiency Disease Specialists
Subject Areas : medical documentsSedigheh Mohammadesmaeil 1 , Shiba Kianmehr 2
1 - Associate Professor, Department of Information Science and Research, Research Sciences Branch, Islamic Azad University, Tehran, Iran
2 - PhD student in Library and Medical Information, Tehran University of Medical Sciences, Tehran, Iran
Keywords: Pediatric Medical Center, Artificial Neural Network, Informational therapy, data mining, Immunodeficiency diseases,
Abstract :
Introduction: This study analyzed the information therapy behavior of immunologists in the country, based on the Cohennon self-organized neural network model. Method: Applied research has been done by descriptive survey method using neural network technique. The tool is a researcher made-questionnaire that was distributed among 149 people. Using MATLAB software, specialists based on the main components of clustering research, and then by removing each of the main sub-components,, the most effective and least effective option was determined. Results: Analysis showed in information retrieval skills; 63.75% of the population are in the first cluster with an average of 29.88 and in the second cluster 36.24% with an average score of 30.22, and the most important component is the use of keywords and terms related to the required information. About ways to get information; 22.14% of the population with an average score of 54.36 in the first cluster, 18.12% of individuals with an average of 48.11 in the second cluster, 14.09% with an average of 43.28 in the third cluster, 16.1% with an average of 0.04 49 were in the fourth cluster and 29.53% of the people with an average score of 53.72 were in the fifth cluster, and the most important way to find information was to use electronic information sources. Based on the use of various information services, 46% of people with an average score of 54.85 in the first cluster, 20.66% with an average of 49.38 in the second cluster and 32.66% with an average of 43.08 in the third cluster and the most important component of information therapy services has been familiarity with various sources and information services in the specialized field. Conclusion: Neural clustering of information therapy behaviors of the study population and the resulting information transactions, in addition to resulting in awareness of the needs and information resources required by users, as an accessible and low-cost method that improves the quality of information of immunodeficiency specialists leads to the provision of more effective medical services to patients, provides the necessary basis for anticipating information-oriented arrangements and decisions to meet the needs and information carriers requested by users of medical databases and provides managers and staff This field, and as an effective strategy with the highest level of possible standards, leads to the discovery of the intangible pattern of information seeking behaviors of health users, and teaches the audience to use information media intelligently.
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_||_1- Parhamnia F. Introduction to Information. Kermanshah: Islamic Azad University; 2008. [In Persian]
2- Moghaddassi H, Hoseini A, Asadi F, Jahanbakhsh M. Application of Data Mining. Heal Inf Manag. 2012:9(2):304. [In Persian]
3- Nabovati E, Azizi A, Abbasi E, Vakili-Arki H, Zarei J, Razavi A. Using Data Mining to Predict Outcome in Burn Patients: A Comparison between Several Algorithms. Heal Inf Manag. 10(6):799. [In Persian]
4- David Lewis A, Delan D. Advanced Data Mining. Tehran: Kerman University Jihad. 2010.
5- Shahrabi J. Data Mining Concepts in Arak. Tehran: Metallon. 2008. [In Persian]
6- Naimi J, Mohammad Ismail S, Heydari H. Determining Information Needs and Information Behavior of Khorasan Razavi Seminary Students Using Neural Network Approach. J Libr Inf Sci. 2019:4(1):118–91. [In Persian]
7- Naimi J, Mohammad Ismail S. Determining Information Seeking Behavior of Khorasan Razavi University of Medical Sciences Students Using Neural Network Approach. J Libr Inf Sci. 2016:6(2):80–96. [In Persian]
8- Badr A, Esmaeil SM, Heidari H. Applying data mining technique in order to categorize the target users of the Central Library of Isfahan University of Technology (Studying the motives and information seeking behaviors of them). Iran J Inf Process Manag. 2017:33(1):275–98. [In Persian]
9- Alizadeh S, Malek Mohammadi Q. Data Mining and Step-by-Step Knowledge Discovery with Clementine Software. Tehran: Khaje Nasir al-Din Tusi University. 2011. [In Persian]
10- Zare_Farashbandi F, Yarahmadi A. Information Therapy: A New Approach with Old Concept in Improvement of Chronic DiseasesNo Title. Heal Inf Manag. 2015:12(1):135. [In Persian]
11- Ghazanfari M, Alizadeh S, Timourpour B. Data Mining and Knowledge Discovery. Tehran: Iran University of Science and Technology: 2011. [In Persian]
12- WHO. Health Promoting Hospitals [Internet]. [cited 2004 Mar 17]. Available from: http://www.euro.who.int/eprise/main/ who /progs/hph/home
13- Nutbeam D. Health Promotion Glossary, Health Promotion International. Oxford Univ Press. 2003:13(4):349–64.