BackgroundPhysicians’ guideline use rates for diagnosis, treatment and monitoring of diabetes mellitus (DM) is very low. Time constraints, patient overpopulation, and complex guidelines require alternative solutions for real time patient monitoring. Rapidly evolving e-health technology combined with clinical decision support and monitoring systems (CDSMS) provides an effective solution to these problems. The purpose of the study is to develop a user-friendly, comprehensive, fully integrated web and mobile-based Clinical Decision Support and Monitoring System (CDSMS) for the screening, diagnosis, treatment, and monitoring of DM diseases which is used by physicians and patients in primary care and to determine the effectiveness of the system.MethodsThe CDSMS will be based on evidence-based guidelines for DM disease. A web and mobile-based application will be developed in which the physician will remotely monitor patient data through mobile applications in real time.The developed CDSMS will be tested in two stages. In the first stage, the usability, understandability, and adequacy of the application will be determined. Five primary care physicians will use the developed application for at least 16 DM patients. Necessary improvements will be made according to physician feedback. In the second phase, a parallel, single-blind, randomized controlled trial will be implemented. DM diagnosed patients will be recruited for the CDSMS trial by their primary care physicians. Ten physicians and their 439 patients will be involved in the study. Eligible participants will be assigned to intervention and control groups with simple randomization. The significance level will be accepted as p < 0.05. In the intervention group, the system will make recommendations on patient monitoring, diagnosis, and treatment. These recommendations will be implemented at the physician’s discretion. Patients in the control group will be treated by physicians according to current DM treatment standards. Patients in both groups will be monitored for 6 months. Patient data will be compared between 0th and 6th month of the study. . Clinical and laboratory outcomes will be assessed in person while others will be self-assessed online.DiscussionThe developed system will be the first of its kind to utilize evidence based guidelines to provide health services to DM patients.Trial registrationClinicalTrials.gov NCT02917226. 28 September 2016.

Web servisleri, tanımlanabilen, yayımlanabilen ve standart Web protokolleri ile ağ üzerinden erişilebilen platform bağımsız, özerk hesaplama birimleridir. Günümüzde Web servisleri, iş dünyasında dağıtık uygulama geliştirme standardı olarak kabul görmektedirler. Ancak Web servisleri daha çok insanlar tarafından bulunmakta ve geliştirilen uygulamaya statik bir şekilde gömülerek kullanılmaktadır. Halbuki ihtiyaç duyulan işlevi yerine getiren fakat farklı arayüzlere sahip birçok Web servisi olabilir ve zaman içerisinde yenileri de eklenebilir. Dolayısıyla bu Web servislerinin uygulamalar tarafından otomatik olarak bulunması ve dinamik bir şekilde çalıştırılması gerekmektedir. Ancak Web servislerinin otomatik olarak bulunması, birleştirilmesi, çalıştırılması ve izlenmesi için, anlamsal Web adı verilen yeni nesil Web vizyonunun sunmuş olduğu ontolojiler kullanılarak modellenmeleri gerekmektedir. Bu servislere ise anlamsal Web servisleri denilmektedir. Dolayısıyla var olan Web servislerinin anlamsal Web servislerine dönüştürülerek yayınlanması ve ihtiyaç duyulduğunda dinamik olarak keşfedilmesi, seçilmesi, çağrılması, izlenmesi gibi işlevleri yerine getirecek araçlara ihtiyaç vardır. Bu çalışmada, servis istemcilerinin, anlamsal mesajlar ile Web servis işletimi yapabilmeleri için bir dinamik Web servis çağrım yöntemi önerilmiştir. Önerilen yöntem için, SAWSDL kullanılarak tanımlanmış olan anlamsal Web servislerinin dinamik olarak çağrımlarının yapılabildiği bir Web servis işletim çerçevesi geliştirilmiştir. Ayrıca önerilen yöntemin daha iyi açıklanması için ve geliştirilen çerçevenin işleyişini göstermesi için müşteri ilişkileri yönetimi alanında örnek bir durum çalışması yapılmıştır.

Renewable energy becomes progressively popular in the world because renewable resources such as solar, geothermal, wind energy are clean, inexhaustible and come from natural sources. Wind energy is one of the most significant resources of renewable energy and it plays a key role in the generation of electricity. Thus, accurate wind power estimation is crucial to deal with the challenges to balance energy trading, planning, scheduling decisions and strategies of wind power generation. This study proposes a prediction model to solve a real-life problem in the renewable energy sector by accurately estimating the amount of wind energy production per hour in the next 24 hours by applying machine learning (ML) techniques using historical wind power generation data and weather forecasting reports. In the proposed approach, first, an unsupervised ML method (i.e., the K-Means clustering algorithm) is applied to group data into meaningful clusters; then, these clusters are accepted as new feature values and added to the dataset to enlarge it; finally, a supervised ML method (i.e., regression) is performed for prediction. This study compares nine supervised learning algorithms: K-Nearest Neighbors, Support Vector Regression, Random Forest, Extra Trees, Gradient Boosting, Ridge Regression, Least Absolute Shrinkage and Selection Operator, Decision Tree, and Convolutional Neural Network. The aim of this study is to investigate the success of different ML algorithms on real-world data of wind turbines and propose a methodology to benchmark various machine learning algorithms to choose the most accurate final model for wind power generation prediction.