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Kavramsal Olarak Tıbbi Bilişim ve Alan Bazlı Tıp Bilişimi Araştırmaları: Türkiye Örneği

Yıl 2024, Cilt: 26 Sayı: 1, 44 - 55, 30.04.2024

Muhammet Damar Tuncay Küme İbrahim Yüksel Ali Emre Çetinkol Jiban K. Pal Fatih Safa Erenay

https://doi.org/10.18678/dtfd.1410276

Öz

Amaç: Bu çalışmada, Tıp Bilişimi araştırmalarını bibliyometrik yöntemler ile analiz ederek Türkiye'nin Tıp Bilişimi alanındaki konumunu ve araştırma genel yapısını değerlendirmeyi amaçlanmıştır.
Gereç ve Yöntemler: Bu çalışmada, R bibliyometrix ve VosViewer aracılığı ile Web of Science bibliyometrik veri kaynağından 1980 ile 2023 yılları arasında üretilen araştırma ve derleme türündeki makaleler bibliyometrik yöntemler ile analiz edilmiştir.
Bulgular: Türkiye tıp bilişimi araştırma alanında, 1980 ile 2023 yılları arasında yapılan bibliyometrik analize göre, 905 makale, 15610 atıf ve ilgili makalelere verilen 17,25 atıf ortalaması, 51 gibi yüksek bir etki değeri ile 27. sırada yer almaktadır. İlgili alanda öne çıkan kurumlar arasında, Ortadoğu Teknik Üniversitesi, Hacettepe Üniversitesi ve Selçuk Üniversitesi bulunmaktadır. Öne çıkan araştırma konuları arasında yoğun araştırma alanlarını yansıtan "sinir ağları, makine öğrenimi, destek vektörü, sağlık hizmetleri, karar desteği, derin öğrenme, EEG sinyalleri, sınıflandırma doğruluğu" yer almaktadır.
Sonuç: Türkiye’de tıp bilişimi uzmanlık alanı temel mühendislik bilimlerine veya tıp bilimlerine göre biraz daha geride kalmıştır. Alanın bilgisayar bilimleri, yazılım mühendisliği, endüstri mühendisliği, yapay zekâ mühendisliği ve elektronik mühendisliği gibi pek çok farklı mühendislik alanı ile kesişen multidisipliner bir dokusu mevcuttur. Bu alanda daha etkin üretkenlik için alanın diğer araştırma alanları ile daha fazla ilişkiye geçilebilir. Ayrıca tıp bilişimi veya sağlık bilişimine ilişkin ivedi olarak dört yıllık lisans programlarının üniversitelerde kurulması önerilmektedir.

Anahtar Kelimeler

Türkiye Tıp Bilişimi Bilimsel Üretkenlik Bibliyometri Atıf Analizi

Kaynakça

  • Atilla EA, Seyhan F. An academic examination of the development of health informatics in Turkey. SDU Visionary Journal. 2022;13(34):364-81. Turkish.
  • Masic I. The history of medical informatics development - an overview. Int J Biomed Healthc. 2020;8(1):37-52.
  • Wyatt JC, Liu JL. Basic concepts in medical informatics. J Epidemiol Community Health. 2002;56(11):808-12.
  • Lincoln TL. Medical informatics: the substantive discipline behind health care computer systems. Int J Biomed Comput. 1990;26(1-2):73-92.
  • Haux R. Health and medical informatics education: perspectives for the next decade. Int J Med Inform. 1998;50(1-3):7-19.
  • Haux R. Medical informatics: past, present, future. Int J Med Inform. 2010;79(9):599-610.
  • van Bemmel JH, Duisterhout JS. Education and training of medical informatics in the medical curriculum. Int J Med Inform. 1998;50(1-3):49-58.
  • Masic I, Pandza, H. Medical informatics education - past, today and future. Eur J Biomed Inform. 2018;14(2):40-45.
  • Kuzeci E. eHealth and new legal problems. InU Law Review. 2018;9(1):477-506. Turkish.
  • Mutluay E, Ozdemir, L. Use of nursing informatics within the scope of health information systems. Florence Nightingale J Nurs 2014;22(3):180-6. Turkish.
  • Ozata M. Importance of health information systems increasing of hospital efficiency: an application used data envelopment analysis. Journal of Productivity. 2009;4:37-51. Turkish.
  • Peker M. A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and SVM. J Med Syst. 2016;40(5):116.
  • Sengul Y. Health informatics infrastructure development of the public space and e-health services in Turkey. J Health Soc Welf Res. 2019;1(2):14-20. Turkish.
  • Yucel YB, Aytekin A, Ayaz A, Tumincin F. The importance of health sectors of information systems. Eurasian J Res Soc Econ. 2018;5(8):147-55. Turkish.
  • Armfield NR, Edirippulige S, Caffery LJ, Bradford NK, Grey JW, Smith AC. Telemedicine--a bibliometric and content analysis of 17,932 publication records. Int J Med Inform. 2014;83(10):715-25.
  • Chen X, Xie H, Wang FL, Liu Z, Xu J, Hao T. A bibliometric analysis of natural language processing in medical research. BMC Med Inform Decis Mak. 2018;18(Suppl 1):14.
  • Eckert M, Volmerg JS, Friedrich CM. Augmented reality in medicine: systematic and bibliographic review. JMIR Mhealth Uhealth. 2019;7(4):e10967.
  • Guo Y, Hao Z, Zhao S, Gong J, Yang F. Artificial intelligence in health care: bibliometric analysis. J Med Internet Res. 2020;22(7):e18228.
  • Hsu YH, Ho YS. Highly cited articles in health care sciences and services field in Science Citation Index Expanded. A bibliometric analysis for 1958 - 2012. Methods Inf Med. 2014;53(6):446-58.
  • Kim J, Lee D, Park E. Machine learning for mental health in social media: bibliometric study. J Med Internet Res. 2021;23(3):e24870.
  • Pawassar CM, Tiberius V. Virtual reality in health care: bibliometric analysis. JMIR Serious Games. 2021;9(4):e32721.
  • Shaikh AK, Alhashmi SM, Khalique N, Khedr AM, Raahemifar K, Bukhari S. Bibliometric analysis on the adoption of artificial intelligence applications in the e-health sector. Digit Health. 2023;9:20552076221149296.
  • Sweileh WM, Al-Jabi SW, AbuTaha AS, Zyoud SH, Anayah FMA, Sawalha AF. Bibliometric analysis of worldwide scientific literature in mobile - health: 2006-2016. BMC Med Inform Decis Mak. 2017;17(1):72.
  • Tang R, Zhang S, Ding C, Zhu M, Gao Y. Artificial intelligence in intensive care medicine: bibliometric analysis. J Med Internet Res. 2022;24(11):e42185.
  • Yang YT, Iqbal U, Ching JH, Ting JB, Chiu HT, Tamashiro H, Hsu YH. Trends in the growth of literature of telemedicine: A bibliometric analysis. Comput Methods Programs Biomed. 2015;122(3):471-9.
  • Al U, Sezen U, Soydal I. The evaluation of scientific publications of Hacettepe University using social network analysis method. HU J Fac Lett. 2012;29(1):53-71. Turkish.
  • Bilik O, Turhan Damar HT, Ozdagoglu G, Ozdagoglu A, Damar M. Identifying trends, patterns, and collaborations in nursing career research: A bibliometric snapshot (1980-2017). Collegian. 2020;27(1):40-8.
  • Abafe EA, Bahta YT, Jordaan H. Exploring biblioshiny for historical assessment of global research on sustainable use of water in agriculture. Sustainability. 2022;14(17):10651.
  • Garfield E. Bradford’s law and related statistical patterns. Essays. 1980;4(19):476-83.
  • Akal F, Batu ED, Sonmez HE, Karadag SG, Demir F, Ayaz NA, et al. Diagnosing growing pains in children by using machine learning: a cross-sectional multicenter study. Med Biol Eng Comput. 2022;60(12):3601-14.
  • Mikhailova V, Anbarjafari G. Comparative analysis of classification algorithms on the breast cancer recurrence using machine learning. Med Biol Eng Comput. 2022;60(9):2589-600.
  • Karapinar Senturk Z. Layer recurrent neural network-based diagnosis of Parkinson's disease using voice features. Biomed Tech (Berl). 2022;67(4):249-66.
  • Durak S, Bayram B, Bakirman T, Erkut M, Dogan M, Gurturk M, et al. Deep neural network approaches for detecting gastric polyps in endoscopic images. Med Biol Eng Comput. 2021;59(7-8):1563-74.
  • Hatipoglu N, Bilgin G. Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships. Med Biol Eng Comput. 2017;55(10):1829-48.
  • Ibrahim MH, Hacibeyoglu M, Agaoglu A, Ucar F. Glaucoma disease diagnosis with an artificial algae-based deep learning algorithm. Med Biol Eng Comput. 2022;60(3):785-96.
  • Polat H, Aluçlu MU, Ozerdem MS. Evaluation of potential auras in generalized epilepsy from EEG signals using deep convolutional neural networks and time-frequency representation. Biomed Tech (Berl). 2020;65(4):379-91.
  • Cengiz E, Kelek MM, Oguz Y, Yilmaz C. Classification of breast cancer with deep learning from noisy images using wavelet transform. Biomed Tech (Berl). 2022;67(2):143-50.
  • Ileri R, Latifoglu F, Demirci E. A novel approach for detection of dyslexia using convolutional neural network with EOG signals. Med Biol Eng Comput. 2022;60(11):3041-55.
  • Kuru K, Niranjan M, Tunca Y, Osvank E, Azim T. Biomedical visual data analysis to build an intelligent diagnostic decision support system in medical genetics. Artif Intell Med. 2014;62(2):105-18.
  • Dag O, Kasikci M, Ilk O, Yesiltepe M. GeneSelectML: a comprehensive way of gene selection for RNA-Seq data via machine learning algorithms. Med Biol Eng Comput. 2023;61(1):229-41.
  • Hariharan M, Polat K, Sindhu R. A new hybrid intelligent system for accurate detection of Parkinson's disease. Comput Methods Programs Biomed. 2014;113(3):904-13.
  • Köse C, Sevik U, Ikibas C, Erdol H. Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images. Comput Methods Programs Biomed. 2012;107(2):274-93.
  • Doruk RO. Feedback controlled electrical nerve stimulation: a computer simulation. Comput Methods Programs Biomed. 2010;99(1):98-112.
  • Yılmaz B, Ciftci E. An FDTD-based computer simulation platform for shock wave propagation in electrohydraulic lithotripsy. Comput Methods Programs Biomed. 2013;110(3):389-98.
  • Albayrak A, Bilgin G. Automatic cell segmentation in histopathological images via two-staged superpixel-based algorithms. Med Biol Eng Comput. 2019;57(3):653-65.
  • Akkoc B, Arslan A, Kok H. Automatic gender determination from 3D digital maxillary tooth plaster models based on the random forest algorithm and discrete cosine transform. Comput Methods Programs Biomed. 2017;143:59-65.
  • Ozkan IA, Koklu M, Sert IU. Diagnosis of urinary tract infection based on artificial intelligence methods. Comput Methods Programs Biomed. 2018;166:51-9.
  • Bayrak T, Cetin Z, Saygili EI, Ogul H. Identifying the tumor location-associated candidate genes in development of new drugs for colorectal cancer using machine-learning-based approach. Med Biol Eng Comput. 2022;60(10):2877-97.
  • Beheshti I, Demirel H, Farokhian F, Yang C, Matsuda H; Alzheimer's Disease Neuroimaging Initiative. Structural MRI-based detection of Alzheimer's disease using feature ranking and classification error. Comput Methods Programs Biomed. 2016;137:177-93.
  • Ozbay E, Altunbey Ozbay F. Interpretable features fusion with precision MRI images deep hashing for brain tumor detection. Comput Methods Programs Biomed. 2023;231:107387.
  • Sailunaz K, Alhajj S, Ozyer T, Rokne J, Alhajj R. A survey on brain tumor image analysis. Med Biol Eng Comput. 2024;62(1):1-45.
  • Suner A, Celikoglu CC, Dicle O, Sokmen S. Sequential decision tree using the analytic hierarchy process for decision support in rectal cancer. Artif Intell Med. 2012;56(1):59-68.
  • Tunc HC, Sakar CO, Apaydin H, Serbes G, Gunduz A, Tutuncu M, et al. Estimation of Parkinson's disease severity using speech features and extreme gradient boosting. Med Biol Eng Comput. 2020;58(11):2757-73.
  • Turhan G, Kucuk H, Isik EO. Spatio-temporal convolution for classification of Alzheimer disease and mild cognitive impairment. Comput Methods Programs Biomed. 2022;221:106825.
  • Yengec-Tasdemir SB, Aydin Z, Akay E, Dogan S, Yilmaz B. Improved classification of colorectal polyps on histopathological images with ensemble learning and stain normalization. Comput Methods Programs Biomed. 2023;232:107441.
  • Akgundogdu A, Jennane R, Aufort G, Benhamou CL, Ucan ON. 3D image analysis and artificial intelligence for bone disease classification. J Med Syst. 2010;34(5):815-28.
  • Aslan K, Bozdemir H, Sahin C, Ogulata SN, Erol R. A radial basis function neural network model for classification of epilepsy using EEG signals. J Med Syst. 2008;32(5):403-8.
  • Ay B, Yildirim O, Talo M, Baloglu UB, Aydin G, Puthankattil SD, et al. Automated depression detection using deep representation and sequence learning with EEG signals. J Med Syst. 2019;43(7):205.
  • Barlas T, Ecem Avci D, Cinici B, Ozkilicaslan H, Muhittin Yalcin M, Eroglu Altinova A. The quality and reliability analysis of YouTube videos about insulin resistance. Int J Med Inform. 2023;170:104960.
  • Beyan OD, Baykal N. A knowledge based search tool for performance measures in health care systems. J Med Syst. 2012;36(1):201-21.
  • Bozkurt S, Zayim N, Gulkesen KH, Samur MK, Karaagaoglu N, Saka O. Usability of a web-based personal nutrition management tool. Inform Health Soc Care. 2011;36(4):190-205.
  • Avdal EU, Kizilci S, Demirel N. The effects of web-based diabetes education on diabetes care results: a randomized control study. Comput Inform Nurs. 2011;29(2):101-6.
  • Ocak H. A medical decision support system based on support vector machines and the genetic algorithm for the evaluation of fetal well-being. J Med Syst. 2013;37(2):9913.
  • Ayar D, Ozalp Gerceker G, Ozdemir EZ, Bektas M. The effect of problematic internet use, social appearance anxiety, and social media use on nursing students' nomophobia levels. Comput Inform Nurs. 2018;36(12):589-95.
  • Ilaslan E, Ozer Z. Web-based training and telephone follow-up of patients with heart failure: randomized controlled trial. Comput Inform Nurs. 2021;40(2):82-9.
  • Kaya N. Factors affecting nurses' attitudes toward computers in healthcare. Comput Inform Nurs. 2011;29(2):121-9.
  • Turan N, Kaya H, Durgun H, Asti T. Nursing students' technological equipment usage and individual innovation levels. Comput Inform Nurs. 2019;37(6):298-305.
  • Aksoy E. Comparing the effects on learning outcomes of tablet-based and virtual reality-based serious gaming modules for basic life support training: randomized trial. JMIR Serious Games. 2019;7(2):e13442.
  • Kisa A. The Turkish commercial health insurance industry. J Med Syst. 2001;25(4):233-9.

Medical Informatics as a Concept and Field-Based Medical Informatics Research: The Case of Turkey

Yıl 2024, Cilt: 26 Sayı: 1, 44 - 55, 30.04.2024

Muhammet Damar Tuncay Küme İbrahim Yüksel Ali Emre Çetinkol Jiban K. Pal Fatih Safa Erenay

https://doi.org/10.18678/dtfd.1410276

Öz

Aim: This study aimed to evaluate the position of Turkey in the field of Medical Informatics and assess the general structure of research by analyzing Medical Informatics research with bibliometric methods.
Material and Methods: In this study, we conducted a bibliometric analysis of research and review articles generated between 1980 and 2023 from the Web of Science bibliometric data source, utilizing bibliometric methods through the R bibliometrix tool and VosViewer.
Results: In the field of medical informatics research in Turkey, the country holds the 27th position with 905 articles, 15,610 citations, and an impressive impact factor of 51, along with an average citation rate of 17.25 per article, based on bibliometric analysis conducted between 1980 and 2023. Notable institutions in this field include Middle East Technical University, Hacettepe University, and Selçuk University. The prominent research topics encompass "neural network(s), machine learning, support vector, health care, decision support, deep learning, EEG signals, classification accuracy," reflecting the areas of intensive investigation.
Conclusion: In Turkey, the field of medical informatics has lagged slightly behind basic engineering sciences or medical sciences. The domain exhibits a multidisciplinary structure intersecting with various engineering fields such as computer science, software engineering, industrial engineering, artificial intelligence engineering, and electronic engineering. To enhance productivity in this field, greater collaboration with other research areas can be pursued. Additionally, it is recommended to urgently establish four-year undergraduate programs specifically dedicated to medical informatics or health informatics at universities.

Anahtar Kelimeler

Turkey Medical informatics Scientific productivity Bibliometrics Citation analysis

Kaynakça

  • Atilla EA, Seyhan F. An academic examination of the development of health informatics in Turkey. SDU Visionary Journal. 2022;13(34):364-81. Turkish.
  • Masic I. The history of medical informatics development - an overview. Int J Biomed Healthc. 2020;8(1):37-52.
  • Wyatt JC, Liu JL. Basic concepts in medical informatics. J Epidemiol Community Health. 2002;56(11):808-12.
  • Lincoln TL. Medical informatics: the substantive discipline behind health care computer systems. Int J Biomed Comput. 1990;26(1-2):73-92.
  • Haux R. Health and medical informatics education: perspectives for the next decade. Int J Med Inform. 1998;50(1-3):7-19.
  • Haux R. Medical informatics: past, present, future. Int J Med Inform. 2010;79(9):599-610.
  • van Bemmel JH, Duisterhout JS. Education and training of medical informatics in the medical curriculum. Int J Med Inform. 1998;50(1-3):49-58.
  • Masic I, Pandza, H. Medical informatics education - past, today and future. Eur J Biomed Inform. 2018;14(2):40-45.
  • Kuzeci E. eHealth and new legal problems. InU Law Review. 2018;9(1):477-506. Turkish.
  • Mutluay E, Ozdemir, L. Use of nursing informatics within the scope of health information systems. Florence Nightingale J Nurs 2014;22(3):180-6. Turkish.
  • Ozata M. Importance of health information systems increasing of hospital efficiency: an application used data envelopment analysis. Journal of Productivity. 2009;4:37-51. Turkish.
  • Peker M. A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and SVM. J Med Syst. 2016;40(5):116.
  • Sengul Y. Health informatics infrastructure development of the public space and e-health services in Turkey. J Health Soc Welf Res. 2019;1(2):14-20. Turkish.
  • Yucel YB, Aytekin A, Ayaz A, Tumincin F. The importance of health sectors of information systems. Eurasian J Res Soc Econ. 2018;5(8):147-55. Turkish.
  • Armfield NR, Edirippulige S, Caffery LJ, Bradford NK, Grey JW, Smith AC. Telemedicine--a bibliometric and content analysis of 17,932 publication records. Int J Med Inform. 2014;83(10):715-25.
  • Chen X, Xie H, Wang FL, Liu Z, Xu J, Hao T. A bibliometric analysis of natural language processing in medical research. BMC Med Inform Decis Mak. 2018;18(Suppl 1):14.
  • Eckert M, Volmerg JS, Friedrich CM. Augmented reality in medicine: systematic and bibliographic review. JMIR Mhealth Uhealth. 2019;7(4):e10967.
  • Guo Y, Hao Z, Zhao S, Gong J, Yang F. Artificial intelligence in health care: bibliometric analysis. J Med Internet Res. 2020;22(7):e18228.
  • Hsu YH, Ho YS. Highly cited articles in health care sciences and services field in Science Citation Index Expanded. A bibliometric analysis for 1958 - 2012. Methods Inf Med. 2014;53(6):446-58.
  • Kim J, Lee D, Park E. Machine learning for mental health in social media: bibliometric study. J Med Internet Res. 2021;23(3):e24870.
  • Pawassar CM, Tiberius V. Virtual reality in health care: bibliometric analysis. JMIR Serious Games. 2021;9(4):e32721.
  • Shaikh AK, Alhashmi SM, Khalique N, Khedr AM, Raahemifar K, Bukhari S. Bibliometric analysis on the adoption of artificial intelligence applications in the e-health sector. Digit Health. 2023;9:20552076221149296.
  • Sweileh WM, Al-Jabi SW, AbuTaha AS, Zyoud SH, Anayah FMA, Sawalha AF. Bibliometric analysis of worldwide scientific literature in mobile - health: 2006-2016. BMC Med Inform Decis Mak. 2017;17(1):72.
  • Tang R, Zhang S, Ding C, Zhu M, Gao Y. Artificial intelligence in intensive care medicine: bibliometric analysis. J Med Internet Res. 2022;24(11):e42185.
  • Yang YT, Iqbal U, Ching JH, Ting JB, Chiu HT, Tamashiro H, Hsu YH. Trends in the growth of literature of telemedicine: A bibliometric analysis. Comput Methods Programs Biomed. 2015;122(3):471-9.
  • Al U, Sezen U, Soydal I. The evaluation of scientific publications of Hacettepe University using social network analysis method. HU J Fac Lett. 2012;29(1):53-71. Turkish.
  • Bilik O, Turhan Damar HT, Ozdagoglu G, Ozdagoglu A, Damar M. Identifying trends, patterns, and collaborations in nursing career research: A bibliometric snapshot (1980-2017). Collegian. 2020;27(1):40-8.
  • Abafe EA, Bahta YT, Jordaan H. Exploring biblioshiny for historical assessment of global research on sustainable use of water in agriculture. Sustainability. 2022;14(17):10651.
  • Garfield E. Bradford’s law and related statistical patterns. Essays. 1980;4(19):476-83.
  • Akal F, Batu ED, Sonmez HE, Karadag SG, Demir F, Ayaz NA, et al. Diagnosing growing pains in children by using machine learning: a cross-sectional multicenter study. Med Biol Eng Comput. 2022;60(12):3601-14.
  • Mikhailova V, Anbarjafari G. Comparative analysis of classification algorithms on the breast cancer recurrence using machine learning. Med Biol Eng Comput. 2022;60(9):2589-600.
  • Karapinar Senturk Z. Layer recurrent neural network-based diagnosis of Parkinson's disease using voice features. Biomed Tech (Berl). 2022;67(4):249-66.
  • Durak S, Bayram B, Bakirman T, Erkut M, Dogan M, Gurturk M, et al. Deep neural network approaches for detecting gastric polyps in endoscopic images. Med Biol Eng Comput. 2021;59(7-8):1563-74.
  • Hatipoglu N, Bilgin G. Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships. Med Biol Eng Comput. 2017;55(10):1829-48.
  • Ibrahim MH, Hacibeyoglu M, Agaoglu A, Ucar F. Glaucoma disease diagnosis with an artificial algae-based deep learning algorithm. Med Biol Eng Comput. 2022;60(3):785-96.
  • Polat H, Aluçlu MU, Ozerdem MS. Evaluation of potential auras in generalized epilepsy from EEG signals using deep convolutional neural networks and time-frequency representation. Biomed Tech (Berl). 2020;65(4):379-91.
  • Cengiz E, Kelek MM, Oguz Y, Yilmaz C. Classification of breast cancer with deep learning from noisy images using wavelet transform. Biomed Tech (Berl). 2022;67(2):143-50.
  • Ileri R, Latifoglu F, Demirci E. A novel approach for detection of dyslexia using convolutional neural network with EOG signals. Med Biol Eng Comput. 2022;60(11):3041-55.
  • Kuru K, Niranjan M, Tunca Y, Osvank E, Azim T. Biomedical visual data analysis to build an intelligent diagnostic decision support system in medical genetics. Artif Intell Med. 2014;62(2):105-18.
  • Dag O, Kasikci M, Ilk O, Yesiltepe M. GeneSelectML: a comprehensive way of gene selection for RNA-Seq data via machine learning algorithms. Med Biol Eng Comput. 2023;61(1):229-41.
  • Hariharan M, Polat K, Sindhu R. A new hybrid intelligent system for accurate detection of Parkinson's disease. Comput Methods Programs Biomed. 2014;113(3):904-13.
  • Köse C, Sevik U, Ikibas C, Erdol H. Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images. Comput Methods Programs Biomed. 2012;107(2):274-93.
  • Doruk RO. Feedback controlled electrical nerve stimulation: a computer simulation. Comput Methods Programs Biomed. 2010;99(1):98-112.
  • Yılmaz B, Ciftci E. An FDTD-based computer simulation platform for shock wave propagation in electrohydraulic lithotripsy. Comput Methods Programs Biomed. 2013;110(3):389-98.
  • Albayrak A, Bilgin G. Automatic cell segmentation in histopathological images via two-staged superpixel-based algorithms. Med Biol Eng Comput. 2019;57(3):653-65.
  • Akkoc B, Arslan A, Kok H. Automatic gender determination from 3D digital maxillary tooth plaster models based on the random forest algorithm and discrete cosine transform. Comput Methods Programs Biomed. 2017;143:59-65.
  • Ozkan IA, Koklu M, Sert IU. Diagnosis of urinary tract infection based on artificial intelligence methods. Comput Methods Programs Biomed. 2018;166:51-9.
  • Bayrak T, Cetin Z, Saygili EI, Ogul H. Identifying the tumor location-associated candidate genes in development of new drugs for colorectal cancer using machine-learning-based approach. Med Biol Eng Comput. 2022;60(10):2877-97.
  • Beheshti I, Demirel H, Farokhian F, Yang C, Matsuda H; Alzheimer's Disease Neuroimaging Initiative. Structural MRI-based detection of Alzheimer's disease using feature ranking and classification error. Comput Methods Programs Biomed. 2016;137:177-93.
  • Ozbay E, Altunbey Ozbay F. Interpretable features fusion with precision MRI images deep hashing for brain tumor detection. Comput Methods Programs Biomed. 2023;231:107387.
  • Sailunaz K, Alhajj S, Ozyer T, Rokne J, Alhajj R. A survey on brain tumor image analysis. Med Biol Eng Comput. 2024;62(1):1-45.
  • Suner A, Celikoglu CC, Dicle O, Sokmen S. Sequential decision tree using the analytic hierarchy process for decision support in rectal cancer. Artif Intell Med. 2012;56(1):59-68.
  • Tunc HC, Sakar CO, Apaydin H, Serbes G, Gunduz A, Tutuncu M, et al. Estimation of Parkinson's disease severity using speech features and extreme gradient boosting. Med Biol Eng Comput. 2020;58(11):2757-73.
  • Turhan G, Kucuk H, Isik EO. Spatio-temporal convolution for classification of Alzheimer disease and mild cognitive impairment. Comput Methods Programs Biomed. 2022;221:106825.
  • Yengec-Tasdemir SB, Aydin Z, Akay E, Dogan S, Yilmaz B. Improved classification of colorectal polyps on histopathological images with ensemble learning and stain normalization. Comput Methods Programs Biomed. 2023;232:107441.
  • Akgundogdu A, Jennane R, Aufort G, Benhamou CL, Ucan ON. 3D image analysis and artificial intelligence for bone disease classification. J Med Syst. 2010;34(5):815-28.
  • Aslan K, Bozdemir H, Sahin C, Ogulata SN, Erol R. A radial basis function neural network model for classification of epilepsy using EEG signals. J Med Syst. 2008;32(5):403-8.
  • Ay B, Yildirim O, Talo M, Baloglu UB, Aydin G, Puthankattil SD, et al. Automated depression detection using deep representation and sequence learning with EEG signals. J Med Syst. 2019;43(7):205.
  • Barlas T, Ecem Avci D, Cinici B, Ozkilicaslan H, Muhittin Yalcin M, Eroglu Altinova A. The quality and reliability analysis of YouTube videos about insulin resistance. Int J Med Inform. 2023;170:104960.
  • Beyan OD, Baykal N. A knowledge based search tool for performance measures in health care systems. J Med Syst. 2012;36(1):201-21.
  • Bozkurt S, Zayim N, Gulkesen KH, Samur MK, Karaagaoglu N, Saka O. Usability of a web-based personal nutrition management tool. Inform Health Soc Care. 2011;36(4):190-205.
  • Avdal EU, Kizilci S, Demirel N. The effects of web-based diabetes education on diabetes care results: a randomized control study. Comput Inform Nurs. 2011;29(2):101-6.
  • Ocak H. A medical decision support system based on support vector machines and the genetic algorithm for the evaluation of fetal well-being. J Med Syst. 2013;37(2):9913.
  • Ayar D, Ozalp Gerceker G, Ozdemir EZ, Bektas M. The effect of problematic internet use, social appearance anxiety, and social media use on nursing students' nomophobia levels. Comput Inform Nurs. 2018;36(12):589-95.
  • Ilaslan E, Ozer Z. Web-based training and telephone follow-up of patients with heart failure: randomized controlled trial. Comput Inform Nurs. 2021;40(2):82-9.
  • Kaya N. Factors affecting nurses' attitudes toward computers in healthcare. Comput Inform Nurs. 2011;29(2):121-9.
  • Turan N, Kaya H, Durgun H, Asti T. Nursing students' technological equipment usage and individual innovation levels. Comput Inform Nurs. 2019;37(6):298-305.
  • Aksoy E. Comparing the effects on learning outcomes of tablet-based and virtual reality-based serious gaming modules for basic life support training: randomized trial. JMIR Serious Games. 2019;7(2):e13442.
  • Kisa A. The Turkish commercial health insurance industry. J Med Syst. 2001;25(4):233-9.
Toplam 69 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Tıbbi Biyoteknoloji (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Muhammet Damar 0000-0002-3985-3073

Tuncay Küme 0000-0003-3440-3513

İbrahim Yüksel 0000-0002-6323-8337

Ali Emre Çetinkol 0000-0002-0694-6871

Jiban K. Pal 0000-0002-2870-9180

Fatih Safa Erenay 0000-0002-3408-0366

Erken Görünüm Tarihi 9 Nisan 2024
Yayımlanma Tarihi 30 Nisan 2024
Gönderilme Tarihi 27 Aralık 2023
Kabul Tarihi 20 Mart 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 26 Sayı: 1

Kaynak Göster

APA Damar, M., Küme, T., Yüksel, İ., Çetinkol, A. E., vd. (2024). Medical Informatics as a Concept and Field-Based Medical Informatics Research: The Case of Turkey. Duzce Medical Journal, 26(1), 44-55. https://doi.org/10.18678/dtfd.1410276
AMA Damar M, Küme T, Yüksel İ, Çetinkol AE, K. Pal J, Safa Erenay F. Medical Informatics as a Concept and Field-Based Medical Informatics Research: The Case of Turkey. Duzce Med J. Nisan 2024;26(1):44-55. doi:10.18678/dtfd.1410276
Chicago Damar, Muhammet, Tuncay Küme, İbrahim Yüksel, Ali Emre Çetinkol, Jiban K. Pal, ve Fatih Safa Erenay. “Medical Informatics As a Concept and Field-Based Medical Informatics Research: The Case of Turkey”. Duzce Medical Journal 26, sy. 1 (Nisan 2024): 44-55. https://doi.org/10.18678/dtfd.1410276.
EndNote Damar M, Küme T, Yüksel İ, Çetinkol AE, K. Pal J, Safa Erenay F (01 Nisan 2024) Medical Informatics as a Concept and Field-Based Medical Informatics Research: The Case of Turkey. Duzce Medical Journal 26 1 44–55.
IEEE M. Damar, T. Küme, İ. Yüksel, A. E. Çetinkol, J. K. Pal, ve F. Safa Erenay, “Medical Informatics as a Concept and Field-Based Medical Informatics Research: The Case of Turkey”, Duzce Med J, c. 26, sy. 1, ss. 44–55, 2024, doi: 10.18678/dtfd.1410276.
ISNAD Damar, Muhammet vd. “Medical Informatics As a Concept and Field-Based Medical Informatics Research: The Case of Turkey”. Duzce Medical Journal 26/1 (Nisan 2024), 44-55. https://doi.org/10.18678/dtfd.1410276.
JAMA Damar M, Küme T, Yüksel İ, Çetinkol AE, K. Pal J, Safa Erenay F. Medical Informatics as a Concept and Field-Based Medical Informatics Research: The Case of Turkey. Duzce Med J. 2024;26:44–55.
MLA Damar, Muhammet vd. “Medical Informatics As a Concept and Field-Based Medical Informatics Research: The Case of Turkey”. Duzce Medical Journal, c. 26, sy. 1, 2024, ss. 44-55, doi:10.18678/dtfd.1410276.
Vancouver Damar M, Küme T, Yüksel İ, Çetinkol AE, K. Pal J, Safa Erenay F. Medical Informatics as a Concept and Field-Based Medical Informatics Research: The Case of Turkey. Duzce Med J. 2024;26(1):44-55.
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