Halk Sağlığı Alanında Makine Öğrenimi Analizinin Kullanımı

Öz

Yaklaşık olarak son on yılda, büyük veri ve yüksek işlem gücündeki ilerlemelerle desteklenen yapay zeka teknolojisi, hızlı bir gelişme göstermiş ve çeşitli uygulama alanlarında olağanüstü bir evreye girmiştir. Makine öğrenimi (MÖ), veri kümelerini kullanarak otomatik olarak öğrenen ve doğru tahminler ve öngörüler elde etmek için insan tarafından denetlenen veya denetlenmeyen sistemler oluşturmak için geliştirilen gelişmiş istatistiksel ve olasılıksal tekniklere dayanmaktadır. Bu yazıda halk sağlığı alanında kullanılan MÖ uygulamalarını araştırmak amaçlanmıştır. Bu uygulamalar 5 başlık altında incelenecektir. Bunlar; sağlık hizmeti kaynaklarının optimizasyonu, sürveyans, salgın tespiti ve acil durum yönetimi, sağlık davranışı analizi ve müdahale, hastalık teşhisi ve prognozu son olarak ise kişiselleştirilmiş tıp. Yıllar içinde teknoloji ilerledikçe, MÖ bu alanlardaki uygulamaların entegrasyonu, sağlık hizmetlerinin planlanması, dönüştürülmesi ve toplum sağlığı sonuçlarının iyileştirilmesinde daha da önemli bir rol oynayacaktır.

Anahtar Kelimeler

• Makine öğrenimi
• Halk Sağlığı
• yapay zeka

The Use of Machine Learning Analysis in Public Health

Öz

In the last decade, supported by advances in big data and high processing power, artificial intelligence technology has rapidly progressed and entered an extraordinary phase in various application areas. Machine Learning (ML) relies on advanced statistical and probabilistic techniques to create automated systems that learn from datasets and generate accurate predictions and forecasts, either supervised or unsupervised by humans. This article aims to explore ML applications in the field of public health, which can be categorized into five main areas: optimization of healthcare resources, surveillance, outbreak detection and emergency management, health behavior analysis and intervention, disease diagnosis and prognosis, and finally, personalized medicine. As technology continues to advance over the years, the integration of ML applications in these areas will play an even more significant role in healthcare planning, transformation, and improving community health outcomes.

Anahtar Kelimeler

• Machine Learning
• Public Health
• artificial intelligence

Kaynakça

1. Adamson, B. et al., 2023. Approach to machine learning for extraction of real-world data variables from electronic health records. Frontiers in Pharmacology, 14(September), pp. 1–12.
2. Ahamed, F., Farid, F., 2019. Applying internet of things and machine-learning for personalized healthcare: Issues and challenges. Proceedings - International Conference on Machine Learning and Data Engineering, 2018, pp. 22–29.
3. Azuaje, F., 2006. Witten IH, Frank E: Data mining: practical machine learning tools and techniques 2nd edition. BioMedical Engineering OnLine, 5(1), pp. 1–3.
4. Barrera, F.J. et al., 2023. Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: a systematic review. Frontiers in Endocrinology, 14(September), p. e1106625.
5. Battineni, G. et al., 2022. Artificial intelligence models in the diagnosis of adult-onset dementia disorders: a review. Bioengineering, 9(8), pp. 1–15.
6. Bhatt, C.M. et al., 2023. Effective heart disease prediction using machine learning techniques. Algorithms, 16(2), p. 88.
7. Callender, T. et al., 2023. Assessing eligibility for lung cancer screening: parsimonious multi-country ensemble machine learning models for lung cancer prediction. PLoS medicine, 20(10), p. e1004287.
8. Chui, K.T. et al., 2017. Disease diagnosis in smart healthcare: Innovation, technologies and applications. Sustainability (Switzerland), 9(12), pp. 1–23.

9. Goh, Y.S. et al., 2022. Machine learning in health promotion and behavioral change: scoping review. Journal of Medical Internet Research, 24(6), p. e35831.
10. Huang, C. et al., 2022. Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019. Socio-Economic Planning Sciences, 80(January), p. e100976.
11. Jamal, A., 2023. Effect of telemedicine use on medical spending and health care utilization: a machine learning approach. AJPM Focus, 2(3), p. e100127.
12. Kourou, K. et al., 2015. Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, pp. 8–17.
13. Lee, M.S. et al., 2023. Transitional zone prostate cancer: performance of texture-based machine learning and image-based deep learning. Medicine, 102(39), p. e35039.
14. Masum, M. et al., 2022. Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management. Socio-Economic Planning Sciences, 80(January), p. e101249.
15. Mitchell, T.M., 2006. The discipline of machine learning. Machine Learning, 17(July), pp. 1–7.
16. Parab, S., Boster, J. and Washington, P., 2023. Parkinson disease recognition using a gamified website: machine learning development and usability study. JMIR Formative Research, 7, p. e49898.
17. Pei, Q. et al., 2022. Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis. Clinical Chemistry and Laboratory Medicine, 60(12), pp. 1974–1983.
18. Pereira, M.A. and Marques, R.C., 2022. Is sunshine regulation the new prescription to brighten up public hospitals in Portugal. Socio-Economic Planning Sciences, 84(January), p. e101219.
19. Rodrigues, P.M. et al., 2021. Lacsogram: a new EEG tool to diagnose Alzheimer’s Disease. IEEE Journal of Biomedical and Health Informatics, 25(9), pp. 3384–3395.
20. Rodrigues, P.M., Madeiro, J.P. and Marques, J.A.L., 2023. Enhancing health and public health through machine learning: decision support for smarter choices. Bioengineering, 10(7), pp. 1–5.
21. Salem, B.S. et al., 2023. Early breast cancer detection and differentiation tool based on tissue impedance characteristics and machine learning. Frontiers in Artificial Intelligence, 6, p. e1248977.
22. Santangelo, O.E. et al., 2023. Machine learning and prediction of infectious diseases: a systematic review. Machine Learning and Knowledge Extraction, 5(1), pp. 175–198.
23. Sebastiani, M. et al., 2022. Personalized medicine and machine learning: a roadmap for the future. Journal of Clinical Medicine, 11(14), pp. 20–24.
24. Shatte, A.B.R., Hutchinson, D.M. and Teague, S.J., 2019. Machine learning in mental health: a scoping review of methods and applications. Psychological Medicine, 49(9), pp. 1426–1448.
25. Tao, X. et al., 2023. Predicting three-month fasting blood glucose and glycated hemoglobin changes in patients with type 2 diabetes mellitus based on multiple machine learning algorithms. Scientific Reports, 13(1), p. e16437.
26. Wang, H. et al., 2023. A machine learning-based PET/CT model for automatic diagnosis of early-stage lung cancer. Frontiers in Oncology, 13(September), pp. 1–10.
27. Zeng, D., Cao, Z. and Neill, D.B., 2020. Artificial intelligence enabled public health surveillance from local detection to global epidemic monitoring and control. Artificial Intelligence in Medicine, 109, pp. 437–453.
28. Zhu, L. et al., 2022. Can artificial intelligence enable the government to respond more effectively to major public health emergencies? Taking the prevention and control of Covid-19 in China as an example. Socio-Economic Planning Sciences, 80(January), p. e101029.