No abstract

Son yıllarda araştırmacılar tarafından makine öğrenmesi algoritmalarını kullanarak sağlık süreçlerinin iyileştirilmesi konusu büyük bir trend haline gelmiştir. Makine öğrenmesi, sağlık hizmetlerin de kaliteyi yükseltmek, hastalık salgınlarını önlemek, hastalıkları erken teşhis etmek, hastane operasyon maliyetlerini azaltmak, hükümete sağlık hizmetleri politikalarında yardımcı olmak ve sağlık hizmetinin verimliliğini artırmak için kullanılan popüler ve etkili bir yöntem haline gelmiştir. Bu derleme çalışmasında, sağlık alanında gerçekleştirilen makine öğrenmesi çalışmaları özetlenmiş ve sınıflandırılmıştır. Özellikle halk sağlığını tehdit eden ve dünya da ölüm nedenleri listesinde ilk sıralarda yer alan, bulaşıcı olmayan hastalık çalışmalarına odaklanılmıştır. Ayrıca dünyanın en büyük ölümcül hastalıklar listesinde yer alan ve son yıllarda halk sağlığı için acil durum ilan edilen Covid-19 hastalığına da yer verilmiştir. Bu çalışmanın amacı, sağlık alanında çalışma yapan araştırmacılara uygun algoritmalarını seçmesinde yardımcı olmaktır. Derleme çalışmasının sonucunda sağlık hizmetlerinde en iyi performans gösteren sınıflandırma algoritması ortalama %100 doğruluk başarısıyla Decision Tree(DT), Random Forest (RF), Gaussian Naive Bayes (GNB) olmuştur.

With the rapid development of technology in recent years, it is observed that there are agile changes in many sectors. With these changes, technology comes to the focus of our lives and helps to take more solid steps by facilitating processes everywhere. With the evolution of the sectors in this direction, concepts such as e-commerce, e-health and data mining have come to the fore, and many studies have been put forward within the framework of these terms. It has been observed that the digital transformation that has begun to take place in the field of healthcare has led to significant changes in this field. The effects of technological advances, which have begun to integrate into health services, such as increasing work efficiency, increasing service quality and creating a safe service environment have been determined. In this review study, various digitalization studies carried out in the field of health between 2012-2022 were examined and summarized, also, the prominent concepts in the studies were classified. In addition, it is aimed to determine the popular methods that researchers include in their studies and to examine the tools that support the application within the scope of the maturity assessment models. At this point, the study is divided into two main headings: (1) Digitalization in Health, (2) Digital Maturity Assessment Models in Health Systems. As a result of the study, it was aimed to contribute to the existing literature by observing the deficiencies in the literature.

In 2019, Coronavirus manifested itself in China and caused numerous deaths. Vaccines developed against COVID-19 are seen as a way to end or mitigate the pandemic. Many debates arose about the vaccination of children through social media. The main target of this study is to present a model that reveals the perception of parents about getting their children vaccinated, extracts the main themes, and determines the emotional changes. With the support of the Octoparse web scraping tool, data was extracted from Twitter when the epidemic turned into a global problem and the discussions about vaccines intensified. Then, using the topic modeling and sentiment analysis techniques under the umbrella of (Natural Language Processing) NLP, main, sub-topics about parents' attitudes were revealed, also vaccine perceptions were detected by performing sentiment analysis. As a result, four topic clusters were determined: “the opinion of the need for the first dose of vaccination according to age”, “the effectiveness of the first dose of vaccine”, “the opinion of the need for vaccination of school-age children”, and “the need for vaccination arising from the protection of unvaccinated children with only mask protection”. With sentiment analysis, it was seen that positive emotions were dominant, and three emotions, namely trust, expectation, and fear, came to the fore. In conclusion, it has been determined that families trust the states and their announcements about getting their children vaccinated, they anticipate new vaccines to be developed, but they are also afraid of the risks that the vaccine will bring to their children.