Metasezgisel Optimizasyon Yöntemlerinin Psikiyatrik Uygulamalardaki Yeri: Bibliyometrik Bir Analiz

Year 2025, Volume: 8 Issue: 4, 1140 - 1151, 15.07.2025

Ümit Can , Feride Tuğrul

Abstract

Mental hastalıklar dünya çapında milyarlarca insanı çeşitli seviyelerde etkilemektedir. Bu durum toplum sağlığı açısından bir risk teşkil etmektedir. Hastalıkların seviyeleri ve etkileri değişiklik gösterse de genel olarak mental rahatsızlıklar kişilerin kendileri ve çevreleri ile sağlıklı bir ilişki kurmalarını engelleyerek günlük yaşamlarını verimli bir şekilde geçirmelerini engeller. Günlük yaşam konforundan uzaklaşan bireyler sosyal ve iş yaşamlarında da birçok zorluk ile karşılaşırlar. Uzun süreli mental rahatsızlık yaşayan insanlarda strese bağlı olarak fizyolojik rahatsızlıklar da görülebilir. Bu durum tedavi edilmediği ve önlenmediği zaman toplum sağlığı problemine dönüşür. Bundan dolayı sağlık alanında faaliyet gösteren sağlık kuruluşları, araştırma merkezleri ve üniversiteler gibi merkezler mental hastalıkların tespiti ve tedavisi için birçok çalışma yapmaktadır. Bu yöntemler geleneksel yöntemleri içermekle birlikte günümüzde yapay zeka destekli yöntemleri de kapsamaktadır. Yapay zeka destekli yöntemler çeşitli türdeki büyük miktarda mental hastalık verisini analiz ederek mental hastalıkların erken teşhisi ve tedavisinde kullanılmaktadır. Araştırmacılar bu yöntemlerin başarılarını geliştirmek için metasezgisel optimizasyon algoritmalarını da kullanmaktadır. Çok karmaşık nonlinear problemlere hızlı ve yüksek başarımlı çözümler arayan bu optimizasyon yöntemleri makine öğrenmesi ve derin öğrenme yöntemlerinin başarılarını arttırmaktadır. Fakat araştırmacılar için bu alanda yapılan çalışmaların sayısını ve niteliğini gösteren ayrı bir bibliyometrik bir çalışma yoktur. Bu çalışma bu alanda oluşan bu boşluğu doldurarak araştırmacılar için ön açısı bir rehber olmayı hedeflemektedir.

Keywords

Bibliyometrik analiz, Metasezgisel optimizasyon, Mental hastalıklar, Mental bozukluklar, Makine öğrenmesi, Derin öğrenme

Ethical Statement

Bu araştırmada hayvanlar ve insanlar üzerinde herhangi bir çalışma yapılmadığı için etik kurul onayı alınmamıştır.

The Position of Metaheuristic Optimization Methods in Psychiatric Practice: A Bibliometric Analysis

Abstract

Mental diseases affect billions of people worldwide to varying degrees. This poses a risk to public health. Although the levels and effects of diseases vary, in general, mental diseases prevent people from having a healthy relationship with themselves and their environment and prevent them from living their daily lives efficiently. Individuals who move away from the comfort of daily life face many difficulties in their social and professional lives. People with long-term mental disorders may also experience physiological disorders due to stress. When this situation is not treated and prevented, it becomes a public health problem. Therefore, centers such as health institutions, research centers, and universities operating in the field of health carry out many studies to detect and treat mental disorders. These methods include traditional methods, but today, they also include artificial intelligence-supported methods. AI-powered methods are used for early diagnosis and treatment of mental illnesses by analyzing large amounts of mental illness data of various types. Researchers are also using metaheuristic optimization algorithms to improve the performance of these methods. These optimization methods, which seek fast and high-performance solutions to very complex nonlinear problems, are increasing the success of machine learning and deep learning methods. Nonetheless, there is no distinct bibliometric analysis demonstrating the quantity and quality of research in this domain for scholars. This study aims to fill this gap in this field and to be a preliminary guide for researchers.

Keywords

Bibliometric analysis, Metaheuristic optimization, Mental diseases, Mental disorders, Machine learning, Deep learning

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