Background: Osteoporosis is a socio-economic problem of modern aging societies. Bone fractures and the related treatments generate the highest costs. The occurrence of osteoporotic fractures is a cause of chronic disability, many complications, reduced quality of life, and often premature death. Aim of the study: The aim of the study was to determine which of the patient’s potential risk factors pertaining to various diseases and lifestyle have an essential impact on the occurrence of low-energy fractures and the hierarchy of these factors. Methods: The study was retrospective. The documentation of 222 patients (206 women and 16 men) from an osteoporosis treatment clinic in Łódź, Poland was analyzed. Each patient was described by a vector consisting of 27 features, where each feature was a different risk factor. Using artificial neural networks, an attempt was made to create a model that, based on the available data, would be able to predict whether the patient would be exposed to low-energy fractures. We developed a neural network model that achieved the best result for the testing data. In addition, we used other methods to solve the classification problem, i.e., correctly dividing patients into two groups: those with fractures and those without fractures. These methods were logistic regression, k-nearest neighbors and SVM. Results: The obtained results gave us the opportunity to assess the effectiveness of various methods and the importance of the features describing patients. Using logistic regression and the recursive elimination of features, a ranking of risk factors was obtained in which the most important were age, chronic kidney disease, neck T-score, and serum phosphate level. Then, we repeated the learning procedure of the neural network considering only these four most important features. The average mean squared error on the test set was about 27% for the best variant of the model. Conclusions: The comparison of the rankings with different numbers of patients shows that the applied method is very sensitive to changes in the considered data (adding new patients significantly changes the result). Further cohort studies with more patients and more advanced methods of machine learning may be needed to identify other significant risk factors and to develop a reliable fracture risk system. The obtained results may contribute to the improved identification patients at risk of low-energy fractures and early implementation of comprehensive treatment.
We propose a new method for analysis of shape optimization of coupled models. The framework of the dual dynamic programming is introduced for a solution of the problems. The shape optimization of coupled model is formulated in terms of characteristic functions which define the suport of control. The construction of ε-optimal solution of such a problem can be obtained by solving the sufficient ε-optimality conditions.
pRzyczyny i SpoSoBy pRzeciwdziaŁania wykLuczeniu cyFRoweMu StreszczenieRozwój technologii informacyjnych jest niewątpliwie jednym z najbardziej dynamicznie rozwijających się obszarów życia. W społeczeństwie powstają różnice między osobami, które posiadają dostęp do technologii informacyjnych, a tymi, które takiego dostępu nie mają. Zjawisko to określa się mianem wykluczenia cyfrowego. W artykule podjęto próbę wskazania przyczyn i sposobów przeciwdziałania wykluczeniu cyfrowemu. Wyodrębniono wiele przyczyn takiego stanu rzeczy w pewnych grupach społecz-nych. Ponadto wskazano na przykładzie sposób przeciwdziałania zjawisku wykluczenia cyfrowego.Słowa kluczowe: wykluczenie cyfrowe, przepaść cyfrowa, Internet, dostęp do Internetu
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