Yapay Zekânın Maliyet Etkililiğini İnceleyen Yayınların Bibliyometrik, Kelime Bulutu ve Duygu Analizi

Year 2023, Volume: 16 Issue: 2, 151 - 165, 30.04.2023

Gülçin Çalışkan , Songül Çınaroğlu

https://doi.org/10.17671/gazibtd.1197021

Cited By: 1

Abstract

Bu çalışma ile, bir karar destek sistemi olarak kullanılan yapay zekânın sağlık sorunlarının tespitinde ortaya koyduğu yöntemin mevcut yönteme göre maliyet etkililiğini tespit eden yayınların ayrıntılı olarak incelenmesi, konuyla ilgili küresel ilginin açığa çıkarılması, yayınların zaman içindeki eğilimlerinin ve hangi konuların daha çok araştırıldığının belirlenmesi amaçlanmıştır. Bununla birlikte bu çalışmanın diğer bir amacı bu yayınlarda en çok tekrar edilen kelimeleri vurgulamak ve yayınları duygu durumuna göre sınıflandırmaktır. Karar destek sistemi olarak kullanılan yapay zekânın sağladığı teşhis ya da tedavi yönteminin klasik teşhis ya da tedavi yöntemine göre maliyet etkililiği ile ilgili literatür Ağustos 2022'ye kadar Web of Science veri tabanında taranmıştır. Dışlama kriterleri uygulandıktan sonra literatür taramasında ulaşılan 24 yayın üzerinden bibliyometrik analiz, kelime bulutu ve duygu analizleri yapılmıştır. Araştırmada çok az sayıda çalışmaya ulaşıldığı ancak son yıllarda konuyla ilgili üretilen yayınların sayısında artış olduğu ve metinlerde en çok tekrar edilen anahtar kelimelerin sırasıyla yapay zekâ, maliyet etkililik, tarama ve makine öğrenimi olduğu tespit edilmiştir. Ayrıca hastalıkların teşhisinde yapay zekâ kullanılarak tanı koymanın klasik tanı koymaya göre maliyet etkililiğini tespit eden çalışmaların en fazla diş çürüğü, atriyal fibrilasyon ve diyabetik retinopati hastalıkları ile ilgili olduğu gözlenmiştir. Bununla birlikte kelime bulutunda en sık tekrar edilen kavramın “tarama” olduğu; duygu analizinde ise genel olarak pozitif duygunun daha ağır bastığı sonucuna ulaşılmıştır.

Keywords

yapay zeka, maliyet etkililik, diyabetik retinopati, atriyal fibrilasyon, diş çürüğü, tarama, bibliyometrik analiz

Bibliometric, Word Cloud and Sentiment Analysis of Publications Examining the Cost Effectiveness of Artificial Intelligence

Abstract

The aim of this study is to examine the publications that determine the cost-effectiveness of the current method compared to the method revealed by artificial intelligence, which is used as a decision support system in the determination of health problems, to reveal the global interest in the topic, to determine the trends of the publications over time and examine which topics are more researched. In addition, another aim of this study is to highlight the most frequently used words in these publications and to classify the publications according to their sentiment. The literature on the cost-effectiveness of the classical diagnosis or treatment method compared to the diagnosis or treatment method provided by artificial intelligence, which is used as a decision support system, was searched in the Web of Science database until August 2022. After the exclusion criteria was applied, bibliometric analysis, word cloud and sentiment analysis were performed on 24 publications reached during the review of the literature. It has been determined that there is a limited number of studies in the research, but there has been an increase in the number of publications on the topic in recent years, and the most frequently used keywords in the texts are artificial intelligence, cost-effectiveness, screening and machine learning, respectively. In addition, it was observed that the studies that determined the cost-effectiveness of diagnosing with artificial intelligence compared to diagnosing with classical method were mostly related to dental caries, atrial fibrillation and diabetic retinopathy diseases. Additionally, the most frequently used word in the word cloud is "screening"; In sentiment analysis, it was concluded that positive sentiment outweigh in general.

Keywords

artificial intelligence, cost-effectiveness, diabetic retinopathy, atrial fibrillation, dental caries, screening, bibliometric analysis

References

• D. Strusani, G.V. Houngbonon, “The Role of Artificial Intelligence in Supporting Development in Emerging Markets”, World Bank Group. 1-8, 2019.
• K. Buntak, M. Kovačić, M. Mutavdžija, “Application of Artificial Intelligence in The Business”, International Journal for Quality Research. 15, 403-416, 2021.
• T. Uzun, "Yapay Zeka Ve Sağlık Uygulamaları", İzmir Katip Çelebi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 3, 1, 80-92, 2020.
• J. Borana, “Applications of Artificial Intelligence & Associated Technologies”, Proceeding of International Conference on Emerging Technologies in Engineering, Biomedical, Management and Science [ETEBMS-2016], 2016.
• J.S. Suri, Mind of An Innovator. Stalk. Artic. 1, 2022. GAO, Artificial Intelligence in Health Care Benefits and Challenges of Machine Learning Technologies for Medical Diagnostics. National Academy of Medicine, 1-84, 2022.
• B. Jena, S. Saxena, G.K. Nayak vd. Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework. J. Cancers 14, 4052, 2022.
• Internet: Appen, How Artificial Intelligence Data Reduces Overhead Costs for Organizations 2022 State Of AI And Machıne Learnıng Report, https://appen.com/blog/how-artificial-intelligence-data-reduces-overhead-costs-for-organizations/, 20.09.2022.
• T. Davenport, & R. Kalakota “The Potential for artificial Intelligence in Healthcare”, Future Healthcare Journal, 6(2), 94–98, 2019.
• J. Gomez Rossi, N. Rojas-Perilla, J. Krois, & F. Schwendicke, “Cost-effectiveness of Artificial Intelligence as a Decision-Support System Applied to the Detection and Grading of Melanoma, Dental Caries, and Diabetic Retinopathy” JAMA Network Open, 5(3), 1-15, 2022.
• X.M. Huang, B.F. Yang, W.L. Zheng, et al. “Cost-Effectiveness of Artificial İntelligence Screening for Diabetic Retinopathy in Rural China” BMC Health Serv Res, 22, 260, 1-12, 2022.
• Y. Xie, Q. Nguyen; V. Bellemo, et. all, "Cost-Effectiveness Analysis of an Artificial Intelligence-Assisted Deep Learning System Implemented in the National Tele-Medicine Diabetic Retinopathy Screening in Singapore", Investigative Ophthalmology & Visual Science, Vol.60, 5471, 2019.
• M. F. Drummond, M. J. Sculpher, K. Claxton, G. L. Stoddart, & G. W. Torrance, Methods for the Economic Evaluation of Health Care Programmes. (4th ed., Oxford University Press, 2015.
• S.D. Shillcutt, D. G. Walker, C. A. Goodman, A. J. Mills, “Cost Effectiveness in Low- And Middle-İncome Countries: A Review of The Debates Surrounding Decision Rules”, PharmacoEconomics, 27(11), 903–917, 2009.
• M. F. Drummond, P. J. Neumann, S. D. Sullivan , F. U. Fricke, S. Tunis, O. Dabbous, &M. Toumi, “Analytic Considerations in Applying a General Economic Evaluation Reference Case to Gene Therapy. Value in Health”, The Journal Of The International Society for Pharmacoeconomics and Outcomes Research, 22(6), 661–668, 2019.
• J.P. Mueller, L. Massaron, “Artificial Intelligence for Dummies”, John Wiley & Sons, Inc., Hoboken, New Jersey,2018.
• N. Soni, E.K. Sharma, N. Singh, A. Kapoor, “Artificial Intelligence in Business From Research and Innovation to Market Deployment” Procedia Computer Science, 167: 2200-2210, 2020.
• Internet: M. Collier, R. Fu, L. Yin, et al. Artificial Intelligence: Healthcare’s New Nervous System Dublin, Ireland: Accenture, https://www.accenture.com/au-en/insights/health/artificial-intelligence-healthcare 11.09.2022.
• T. Bezboruah, A. Bora, “Artificial intelligence: The Technology, Challenges and Applications”, Transactions on Machine Learning and Artificial Intelligence, 8 (5), 45-51, 2020.
• T. Bezboruah, A. Bora, “Artificial intelligence: The Technology, Challenges and Applications”, Transactions on Machine Learning and Artificial Intelligence, 8 (5), 45-51, 2020.
• Internet: McKinsey & Company, Global AI Survey: AI Proves its Worth, But Few Scale İmpact, https://www.mckinsey.com/featured-insights/artificial-intelligence/global-ai-survey-ai-proves-its-worth-but-few-scale-impact, 2 Ekim 2022.
• C. Piccininni, “Cost-Effectiveness of Robotics and Artificial Intelligence in Healthcare”, University of Western Ontario Medical Journal, 87, 49-51, 2019.
• Internet: Frost & Sullivan. (2016). From $600 M to $6 Billion, Artificial Intelligence Systems Poised for Dramatic Market Expansion in Healthcare Mountaın View, Calif. https://www.frost.com/news/press-releases/600-m-6-billion-artificial-intelligence-systems-poised-dramatic-market-expansion-healthcare/ 2.10.2022.
• N.J. Van Eck, L. Waltman, “Software Survey VOSviewer, a Computer Program for Bibliometric Mapping”, Scientometrics, 84(2), 523–538, 2010.
• J. Fan, Y. Gao, N. Zhao, R. Dai, H. Zhang, X. Feng, G. Shi, J. Tian, C. Chen, B.D. Hambly. S. Bao, Bibliometric Analysis on COVID-19: A Comparison of Research Between English and Chinese Studies. Front, Public Health, 8,477, 1-10, 2020.
• I. Zupic, & T. Čater, “Bibliometric Methods in Management and Organization”, Organizational Research Methods, 18(3), 429- 472, 2015.
• U. Al, U. Sezen, & İ. Soydal, Hacettepe Üniversitesi Bilimsel Yayınlarının Sosyal Ağ Analizi Yöntemiyle Değerlendirilmesi, Hacettepe Üniversitesi Edebiyat Fakültesi Dergisi, 29(1), 2012.
• M. McBurney, P. Novak, “ What is Bibliometrics and Why Should You Care?”, IEEE International Professional Communication Conference, 108 - 114. 10.1109/IPCC.2002.1049094, 2022.
• E.C.M. Noyons, “Bibliometric Mapping as A Science and Research Management Tool”, DSWO Press, Leiden University.1999.
• E. Noyons, Bibliometric Mapping of Science in A Policy Context. Scientometrics 50, 83–98, 2001.
• M.N. Kurutkan, F. Orhan, “Sağlık Politikası Konusunun Bilim Haritalama Teknikleri ile Analizi”, İksad Yayınevi, Ankara, 2018.
• D, Gulmez, K. Ozteke, İ.G. Sedat, “Uluslararası Dergilerde Yayımlanan Türkiye Kaynaklı Eğitim Araştırmalarının Genel Görünümü: Bibliyometrik Analiz”, Eğitim ve Bilim, 1-27, 2020.
• R. Atenstaedt, “Word Cloud Analysis of The BJGP”, Br J Gen Pract, 62(596), 148, 2012.
• C. Depaolo, K. Wilkinson, “Get Your Head into the Clouds: Using Word Clouds for Analyzing Qualitative Assessment Data”, TechTrends. 58, 38-44, 2014.
• B. Liu, "Sentiment Analysis and Opinion Mining”, Synthesis Lectures on Human Language Technologies. 5:1, 1-167, 2012.
• J. Reis, P. Olmo, F. Benevenuto, H. Kwak, R. Prates, J. “An, Breaking The News: First Impressions Matter on Online News”, In ICWSM ’15, 2015.
• N. Godbole, M. Srinivasaiah, S. Sekine, Large-Scale Sentiment Analysis for News and Blogs. In International Conference on Weblogs and Social Media, Denver, CO, 2007.
• K. Z. Aung, N. N. Myo, “Sentiment Analysis Of Students' Comment Using Lexicon Based Approach”, 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), 149-154, 2017.
• S. Chamass, H. Hazimeh, J. Makki, E. Mugellini, & O.A. Khaled, “Lexicon-Based Sentiment Analysis Approach for Ranking Event Entities”, International Journal of Services and Standards, 12 (2), 126 – 139, 2018.
• S. Sohangir, N. Petty, & D. Wang, “Financial Sentiment Lexicon Analysis”, In: 2018 IEEE 12th International Conference on Semantic Computing (ICSC), IEEE, 286–289, 2018.
• V. Bonta, N. K. N. Janardhan, “A Comprehensive Study on Lexicon Based Approaches For Sentiment Analysis”, Asian Journal Of Computer Science And Technology. 8 (S2), 1–6, 2019.
• S. Choi, H. Park, J. Yeo, S.W. Hwang, “Less is More: Attention Supervision With Counterfactuals for Text Classification”, In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 6695–6704, 2020.
• Internet: T. Bulut, R’da Duygu Analizi Üzerine Vaka Çalışmaları: Case Studies on Sentiment Analysis in R. https://tevfikbulut.net/rda-duygu-analizi-uzerine-vaka-calismalari-case-studies-on-sentiment-analysis-in-r/
• B. Liu, M. Hu, & J. Cheng, “Opinion Observer: Analyzing and Comparing Opinions on The Web”, In Pro- ceedings of the 14th international conference on World Wide Web, 342-351, 2005.
• F.A. Nielsen, “A new ANEW: Evaluation of A Word List For Sentiment Analysis in Microblogs”, Proceedings of the ESWC2011 Workshop on 'Making Sense of Microposts': Big Things Come in Small Packages 718 in CEUR Workshop Proceedings, 93-98, 2011.
• S. Mohammad, P. Turney, “Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion.
• S. Mohammad, P. Turney, “Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon”, In Proceedings of the NAACL-HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, LA, California,2010.
• Loughran, Tim & Mcdonald, Bill, “When Is a Liability NOT a Liability? Textual Analysis, Dictionaries, and 10-Ks” The Journal of Finance, 66, 35 – 65, 2011.
• M.L. Jockers, T. Rosamond, “Text Analysis with R: For Students of Literature”, Springer Nature Switzerland, 2nd ed. Cham, Switzerland, 2020.
• H. Kim, “Sentiment Analysis: Limits and Progress of the Syuzhet Package and Its Lexicons”, Digital Humanities Quarterly, 16 (2), 1-40, 2022.
• R. Bali, D. Sarkar, T. Sharma, “Learning Social Media Analytics with R”, Packet Publishing Ltd, Birmingham- Mumbai, 2017.
• A. Gönel, İ. Koyuncu, “Elimination of Clinical Biochemistry Laboratory Tests Through Artificial Intelligence Programs To İncrease Cost-Effectiveness”, Journal of Clinical and Analytical Medicine, 346-349, 2018.
• K. Van Nunen, J. Li, G. Reniers, & K. Ponnet, “Bibliometric Analysis of Safety Culture Research”, Safety Science, 108, 248–258, 2018.
• Y. Guo, Z. Hao, S. Zhao, J. Gong, F. Yang, “Artificial Intelligence in Health Care: Bibliometric Analysis”, J Med Internet Res, 22(7), e18228, 2020.
• S. Dinakaran, P. Anitha," A Review and Study on AI in Health Care Issues. International Journal of Scientific Research in Computer Science", Engineering and Information Technology, 281-288, 2018.