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Nowadays, Web Applications (WAs) are complex software sys- tems, used by multiple users with different roles and often devel- oped to support and manage business processes. Due to the chang- ing nature of the supported processes, WAs need to be easily and quickly modified, to adapt and align them to the processes they support. In recent years, Model Driven Engineering (MDE) ap- proaches have been proposed and used to develop and evolveWAs. However, the definition of appropriate MDE approaches for the de- velopment of flexible process-centric WAs is still limited. In par- ticular, (flexible) workflow models have never been integrated with the models (e.g., presentation, information models) used in MDE approaches to develop this type of applications. In this paper, we present M3D (Model Driven Development with Declare), a tool for developingWAs that integrates three MDE metamodels used to represent the main components of aWA with the metamodel of De- clare, a declarative language to model business processes. The tool exploits and combines the declarative nature of Declare and the ad- vantages of MDE to get an efficient roundtrip engineering support to develop and evolve flexible process-centric WAs.

The thyroid is an endocrine gland located in the anterior region of the neck: its main task is to produce thyroid hormones, which are functional to our entire body. Its possible dysfunction can lead to the production of an insufficient or excessive amount of thyroid hormone. Therefore, the thyroid can become inflamed or swollen due to one or more swellings forming inside it. Some of these nodules can be the site of malignant tumors. One of the most used treatments is sodium levothyroxine, also known as LT4, a synthetic thyroid hormone used in the treatment of thyroid disorders and diseases. Predictions about the treatment can be important for supporting endocrinologists’ activities and improve the quality of the patients’ life. To date, there are numerous studies in the literature that focus on the prediction of thyroid diseases on the trend of the hormonal parameters of people. This work, differently, aims to predict the LT4 treatment trend for patients suffering from hypothyroidism. To this end, a dedicated dataset was built that includes medical information related to patients being treated in the ”AOU Federico II” hospital of Naples. For each patient, the clinical history is available over time, and therefore on the basis of the trend of the hormonal parameters and other attributes considered it was possible to predict the course of each patient’s treatment in order to understand if this should be increased or decreased. To conduct this study, we used different machine learning algorithms. In particular, we compared the results of 10 different classifiers. The performances of the different algorithms show good results, especially in the case of the Extra-Tree Classifier, where the accuracy reaches 84%.