Halk Sağlığı Alanında Makine Öğrenimi Analizinin Kullanımı

Year 2024, Volume: 7 Issue: 1, 27 - 29, 29.03.2024

Kübra Ecem Turgutkaya , Emine Didem Evci Kiraz

https://doi.org/10.38016/jista.1374240

Abstract

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.

Keywords

Makine öğrenimi, Halk Sağlığı, yapay zeka

The Use of Machine Learning Analysis in Public Health

Abstract

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.

Keywords

Machine Learning, Public Health, artificial intelligence

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