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Sağlıkta veri kalitesi ve veri madenciliği uygulamaları

Year 2023, Volume: 3 Issue: 1, 23 - 30, 31.01.2023
https://doi.org/10.56723/dyad.1161993

Abstract

Veri günümüzde çok sık karşılaşılan bir terimdir. Verinin doğru kullanımı doğru değerlendirmeyi sağlar. Bu da kaynakların verimli kullanımını, verilen hizmetin kalitesinin artmasını sağlamaktadır. Verinin en çok toplandığı alanların başında sağlık sektörü gelmektedir. Sağlık hizmet sunumunun maddi ve manevi yükü ağırdır. Bu hizmetin en iyi şekilde verilmesi, kaynakların doğru kullanılması ile yakın ilişkilidir. Sağlık verilerinden anlamlı sonuçların çıkarılarak hekimlere, hemşirelere ve sağlık yöneticileri gibi sağlık sektörü çalışanlarına yön gösterecek bilgilerin sağlanması sağlık verilerinin büyüklüğü düşünüldüğünde ancak veri madenciliği metotları ile mümkündür. Sağlık sektörünün insan hayatını direkt etkileyen bir doğası olması sebebi ile sağlıkta kullanılan verilerin kalitesinin en üst düzeyde olması beklenmektedir. Bu çalışmada veri kalitesini ve veri madenciliğini bütüncül olarak ele almıştır. Uygulama örnekleri aracılığıyla veri madenciliği ile sağlık sektöründe ne tür çalışmalar yapılabileceğine dair genel bir bakış açısı sağlanmıştır.

Thanks

Makalemizi inceleyen editör ekibine teşekkür ederim.

References

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  • [3] Shi G. Chapter 1. Data mining and knowledge discovery for geoscientists, 1-22, Elsevier, 2013.
  • [4] Han J, Pei J, Kamber M. Data Mining: Concepts and Techniques, The Morgan Kaufmann Series in Data Management Systems Book, 1-38, 2012.
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  • [6] Gualo F, Rodriguez M, Verdugo J, Caballero I, Piattini M. “Data quality certification using ISO/IEC 25012: Industrial experiences”. Journal of Systems and Software, 176, 110938, 2021.
  • [7] Olson JE. Chapter 1. Data quality: the accuracy dimension, 3-23, Elsevier, 2003.
  • [8] Daneshkohan A, Alimoradi M, Ahmadi M, Alipour J. “Data quality and data use in primary health care: A case study from Iran”. Informatics in Medicine Unlocked, 28, 100855, 2022.
  • [9] Rajan NS, Gouripeddi R, Mo P, Madsen RK, Facelli JC. “Towards a content agnostic computable knowledge repository for data quality assessment”. Computer Methods and Programs in Biomedicine, 177, 193-201, 2019.
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  • [27] Kaur I, Doja MN, Ahmad T. “Data mining and machine learning in cancer survival research: An overview and future recommendations”. Journal of Biomedical Informatics, 128, 104026, 2022.
  • [28] Kirlidog M, Aşuk C. “A Fraud Detection Approach with Data Mining in Health Insurance”. Procedia - Social and Behavioral Sciences, 62, 989-94, 2012.
  • [29] Santos RS, Malheiros SMF, Cavalheiro S, de Oliveira JMP. “A data mining system for providing analytical information on brain tumors to public health decision makers”. Computer Methods and Programs in Biomedicine, 109(3), 269-82, 2013.
  • [30] da Costa NL, de Sá Alves M, de Sá Rodrigues N, Bandeira CM, Oliveira Alves MG, Mendes MA, et al. “Finding the combination of multiple biomarkers to diagnose oral squamous cell carcinoma – A data mining approach”. Computers in Biology and Medicine, 143, 105296, 2022.
  • [31] Wang C-H, Nguyen PA, Li YC, Islam MM, Poly TN, Tran Q-V, et al. “Improved diagnosis-medication association mining to reduce pseudo-associations”. Computer Methods and Programs in Biomedicine, 207, 106181, 2021.
  • [32] Parviainen A, Vázquez-Arias A, Arrebola JP, Martín-Peinado FJ. “Human health risks associated with urban soils in mining areas”. Environmental Research, 206, 112514, 2022.
  • [33] Aljumah AA, Ahamad MG, Siddiqui MK. “Application of data mining: Diabetes health care in young and old patients”. Journal of King Saud University-Computer and Information Sciences, 25(2), 127-36, 2013.
  • [34] Kılınç Ü. Classification of brain MR image data using data mining techniques. Yüksek Lisans Tezi, Adana Bilim ve Teknoloji Üniversitesi, Adana, Türkiye, 2019.
  • [35] Hassani M. Predicting drug synergy using data mining. Doktora Tezi, Sabancı Üniversitesi, İstanbul, Türkiye, 2016.
  • [36] Santos-Pereira, Judith, Le Gruenwald, and Jorge Bernardino. “Top data mining tools for the healthcare industry”. Journal of King Saud University-Computer and Information Sciences, 34(8), 4968-4982, 2022.
  • [37] Rojas E, Munoz-Gama J, Sepúlveda M, Capurro D. “Process mining in healthcare: A literature review”. Journal of biomedical informatics, 61, 224-236, 2016.
  • [38] Srivastava AK, Jeberson K, and Jeberson W. “A systematic review on data mining application in Parkinson's disease”. Neuroscience Informatics, 100064, 2022.
  • [39] Karatas M, Eriskin L, Deveci M, Pamucar D, Garg H. “Big Data for Healthcare Industry 4.0: Applications, challenges and future perspectives”. Expert Systems with Applications, 116912, 2022.

Data quality and data mining applications in healthcare

Year 2023, Volume: 3 Issue: 1, 23 - 30, 31.01.2023
https://doi.org/10.56723/dyad.1161993

Abstract

Data is a common term recently. Correct use of data ensures correct evaluation, which ensures efficient use of resources and increases the quality of the service provided. The health sector is one of the areas where data is collected the most. The financial and moral burden of health service delivery is heavy. Providing this service in the best way is closely related to the correct use of resources. By extracting meaningful results from health data, providing information that will guide health sector workers such as physicians, nurses and health managers is only possible with data mining methods, considering the size of health data. Since the health sector has a nature that directly affects human life, the quality of data used in health is expected to be at the highest level. In this study, data quality and data mining were handled holistically. A general viewpoint on what kind of studies can be done in the health sector with data mining has been provided through application examples.

References

  • [1] Doger Ş. Veri Kalitesinde Eksik Veri Sorunlarının Derin Öğrenme Yöntemi İle Çözülmesi: Üretici Çekişmeli Ağlar İle Bir Uygulama. Yüksek Lisans Tezi, Dokuz Eylül Üniversitesi, İzmir, Türkiye, 2020.
  • [2] Liu Q, Feng G, Zhao X, Wang W. “Minimizing the data quality problem of information systems: A process-based method”. Decision Support Systems, 137, 113381, 2020.
  • [3] Shi G. Chapter 1. Data mining and knowledge discovery for geoscientists, 1-22, Elsevier, 2013.
  • [4] Han J, Pei J, Kamber M. Data Mining: Concepts and Techniques, The Morgan Kaufmann Series in Data Management Systems Book, 1-38, 2012.
  • [5] McCord SE, Welty JL, Courtwright J, Dillon C, Traynor A, Burnett SH et al. Ten practical questions to improve data quality. Rangelands, 44(1), 17-28, 2022.
  • [6] Gualo F, Rodriguez M, Verdugo J, Caballero I, Piattini M. “Data quality certification using ISO/IEC 25012: Industrial experiences”. Journal of Systems and Software, 176, 110938, 2021.
  • [7] Olson JE. Chapter 1. Data quality: the accuracy dimension, 3-23, Elsevier, 2003.
  • [8] Daneshkohan A, Alimoradi M, Ahmadi M, Alipour J. “Data quality and data use in primary health care: A case study from Iran”. Informatics in Medicine Unlocked, 28, 100855, 2022.
  • [9] Rajan NS, Gouripeddi R, Mo P, Madsen RK, Facelli JC. “Towards a content agnostic computable knowledge repository for data quality assessment”. Computer Methods and Programs in Biomedicine, 177, 193-201, 2019.
  • [10] UNECE Sustainable development GOALS. https://unece.org/fileadmin/DAM/stats/documents/ece/ces/2000/11/metis/crp.3.e.pdf (14.12.2022).
  • [11] Türkiye İstatistik Kurumu [TUİK]. “TÜİK Kalite Güvence Çerçevesi Belgesi”. https://www.tuik.gov.tr/Kurumsal/PDF_Detay (23.08.2022).
  • [12] Wang RY & Strong DM. “Beyond accuracy: What data quality means to data consumers”. Journal of management information systems, 12(4), 5-33, 1996.
  • [13] Redman TC. Data quality for the information age. Artech House, Inc. 1997.
  • [14] Dünya Sağlık Örgütü [DSÖ]. “Uluslararası Hastalık Sınıflandırılması, 2022”. https://www.who.int/classifications/classification-of-diseases (20.05.2022).
  • [15] Dünya Sağlık Örgütü [DSÖ]. “Anatomik Terapotik Kimyasal Kodu [ATC] 1948”. https://www.who.int/classifications/classification-of-diseases (06.06.2022).
  • [16] T.C. Çalışma ve Sosyal Güvenlik Bakanlığı Sosyal Güvenlik Kurumu. “Sağlık Uygulama Tebliği, 2013”. https://www.mevzuat.gov.tr/mevzuat?MevzuatNo=17229&MevzuatTur=9&MevzuatTertip=5 (21.05.2022).
  • [17] Mantıksal Gözlem Tanımlayıcı Adları ve Kodları [LOINC]. https://loinc.org/ (21.05.2022).
  • [18] Küresel Medikal Cihaz Sınıflandırma (GMDN). https://www.gmdnagency.org/ (21.05.2022).
  • [19] Han J, Kamber M. and Pei J. Chapter 1 Introduction. Data Mining: Concepts and Techniques. Third Edition, 1-38, The Morgan Kaufmann Series in Data Management Systems Book, 2012.
  • [20] Karimi HA. Big Data: techniques and technologies in geoinformatics, 2, Crc Press, 2014.
  • [21] Zhao Y, Cen Y. Data mining applications with R. Academic Press, 35-7, 2013.
  • [22] Losarwar V, Joshi DM. “Data preprocessing in web usage mining”. International Conference on Artificial Intelligence and Embedded Systems (ICAIES'2012) July (pp. 15-16). Chapter 3, 88-113, 2012.
  • [23] Bekki, A. Sağlık Alanında İstatistik, T.C. Anadolu Üniversitesi Yayını No:3238, 104-106, 2019.
  • [24] Frank E, Hall MA. Chapter 4. Data mining: practical machine learning tools and techniques, 124-127, Morgan Kaufmann, 2011.
  • [25] Han J, Pei J, Kamber, M. Chapter 12. Data mining: concepts and techniques, 543-550, Elsevier, 2011.
  • [26] Hong M, Lu M, Lu C, Zhu Y. “Association analysis of the clinical medical case-set based on the data mining in lung cancer”. Asian Journal of Surgery, 45(5), 1158-1159, 2022.
  • [27] Kaur I, Doja MN, Ahmad T. “Data mining and machine learning in cancer survival research: An overview and future recommendations”. Journal of Biomedical Informatics, 128, 104026, 2022.
  • [28] Kirlidog M, Aşuk C. “A Fraud Detection Approach with Data Mining in Health Insurance”. Procedia - Social and Behavioral Sciences, 62, 989-94, 2012.
  • [29] Santos RS, Malheiros SMF, Cavalheiro S, de Oliveira JMP. “A data mining system for providing analytical information on brain tumors to public health decision makers”. Computer Methods and Programs in Biomedicine, 109(3), 269-82, 2013.
  • [30] da Costa NL, de Sá Alves M, de Sá Rodrigues N, Bandeira CM, Oliveira Alves MG, Mendes MA, et al. “Finding the combination of multiple biomarkers to diagnose oral squamous cell carcinoma – A data mining approach”. Computers in Biology and Medicine, 143, 105296, 2022.
  • [31] Wang C-H, Nguyen PA, Li YC, Islam MM, Poly TN, Tran Q-V, et al. “Improved diagnosis-medication association mining to reduce pseudo-associations”. Computer Methods and Programs in Biomedicine, 207, 106181, 2021.
  • [32] Parviainen A, Vázquez-Arias A, Arrebola JP, Martín-Peinado FJ. “Human health risks associated with urban soils in mining areas”. Environmental Research, 206, 112514, 2022.
  • [33] Aljumah AA, Ahamad MG, Siddiqui MK. “Application of data mining: Diabetes health care in young and old patients”. Journal of King Saud University-Computer and Information Sciences, 25(2), 127-36, 2013.
  • [34] Kılınç Ü. Classification of brain MR image data using data mining techniques. Yüksek Lisans Tezi, Adana Bilim ve Teknoloji Üniversitesi, Adana, Türkiye, 2019.
  • [35] Hassani M. Predicting drug synergy using data mining. Doktora Tezi, Sabancı Üniversitesi, İstanbul, Türkiye, 2016.
  • [36] Santos-Pereira, Judith, Le Gruenwald, and Jorge Bernardino. “Top data mining tools for the healthcare industry”. Journal of King Saud University-Computer and Information Sciences, 34(8), 4968-4982, 2022.
  • [37] Rojas E, Munoz-Gama J, Sepúlveda M, Capurro D. “Process mining in healthcare: A literature review”. Journal of biomedical informatics, 61, 224-236, 2016.
  • [38] Srivastava AK, Jeberson K, and Jeberson W. “A systematic review on data mining application in Parkinson's disease”. Neuroscience Informatics, 100064, 2022.
  • [39] Karatas M, Eriskin L, Deveci M, Pamucar D, Garg H. “Big Data for Healthcare Industry 4.0: Applications, challenges and future perspectives”. Expert Systems with Applications, 116912, 2022.
There are 39 citations in total.

Details

Primary Language Turkish
Subjects Health Care Administration
Journal Section Reviews
Authors

Ahmet Koçak 0000-0003-0754-7773

Prof.dr. Mehmet Ali Ergün 0000-0001-9696-0433

Publication Date January 31, 2023
Submission Date August 14, 2022
Published in Issue Year 2023 Volume: 3 Issue: 1

Cite

APA Koçak, A., & Ergün, P. M. A. (2023). Sağlıkta veri kalitesi ve veri madenciliği uygulamaları. Disiplinlerarası Yenilik Araştırmaları Dergisi, 3(1), 23-30. https://doi.org/10.56723/dyad.1161993
AMA Koçak A, Ergün PMA. Sağlıkta veri kalitesi ve veri madenciliği uygulamaları. Disiplinlerarası Yenilik Araştırmaları Dergisi. January 2023;3(1):23-30. doi:10.56723/dyad.1161993
Chicago Koçak, Ahmet, and Prof.dr. Mehmet Ali Ergün. “Sağlıkta Veri Kalitesi Ve Veri madenciliği Uygulamaları”. Disiplinlerarası Yenilik Araştırmaları Dergisi 3, no. 1 (January 2023): 23-30. https://doi.org/10.56723/dyad.1161993.
EndNote Koçak A, Ergün PMA (January 1, 2023) Sağlıkta veri kalitesi ve veri madenciliği uygulamaları. Disiplinlerarası Yenilik Araştırmaları Dergisi 3 1 23–30.
IEEE A. Koçak and P. M. A. Ergün, “Sağlıkta veri kalitesi ve veri madenciliği uygulamaları”, Disiplinlerarası Yenilik Araştırmaları Dergisi, vol. 3, no. 1, pp. 23–30, 2023, doi: 10.56723/dyad.1161993.
ISNAD Koçak, Ahmet - Ergün, Prof.dr. Mehmet Ali. “Sağlıkta Veri Kalitesi Ve Veri madenciliği Uygulamaları”. Disiplinlerarası Yenilik Araştırmaları Dergisi 3/1 (January 2023), 23-30. https://doi.org/10.56723/dyad.1161993.
JAMA Koçak A, Ergün PMA. Sağlıkta veri kalitesi ve veri madenciliği uygulamaları. Disiplinlerarası Yenilik Araştırmaları Dergisi. 2023;3:23–30.
MLA Koçak, Ahmet and Prof.dr. Mehmet Ali Ergün. “Sağlıkta Veri Kalitesi Ve Veri madenciliği Uygulamaları”. Disiplinlerarası Yenilik Araştırmaları Dergisi, vol. 3, no. 1, 2023, pp. 23-30, doi:10.56723/dyad.1161993.
Vancouver Koçak A, Ergün PMA. Sağlıkta veri kalitesi ve veri madenciliği uygulamaları. Disiplinlerarası Yenilik Araştırmaları Dergisi. 2023;3(1):23-30.