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    Wyświetlanie 1-5 z 5
    Tytuł:
    An improved medical diagnosing of acute abdominal pain with decision tree
    Autorzy:
    Jankowski, D.
    Jackowski, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/333015.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    medyczne systemy wspomagania decyzji
    drzewa decyzyjne
    klasyfikacja
    medical decision support systems
    decision trees
    classification
    Opis:
    In medical decision making (e.g., classification) we expect that decision will be made effectively and reliably. Decision making systems with their ability to learn automatically seem to be very appropriate for performing such tasks. Decision trees provide high classification accuracy with simple representation of gathered knowledge. Those advantages cause that decision trees have been widely used in different areas of medical decision making. In this paper we present characteristic of univariate and multivariate decision tree. We apply those classifiers to the problem of acute abdominal pain diagnosis.
    Źródło:
    Journal of Medical Informatics & Technologies; 2012, 20; 65-71
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Selection of classifier in acute abdominal pain diagnosis with decision tree model
    Autorzy:
    Burduk, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/333503.pdf
    Data publikacji:
    2011
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    teoria podejmowania decyzji Bayesa
    wielostopniowy klasyfikator
    medyczne systemy wspomagania decyzji
    Bayes decision theory
    multistage classifier
    medical decision support systems
    Opis:
    The article presents the application of the decision tree classifier to the acute abdominal pain diagnosis. The recognition task model is based on a decision tree. In this model the decision tree structure is given by the experts. For the assumed structure of the decision tree the locally optimal strategy is considered. The problem discussed in the work shows a selection of different classifiers (parameters) to the internal nodes of the decision tree. Experiments conducted for selected medical diagnosis problem shows that the use of different parameters for k-NN classification can improve the quality of classification in comparison with the situation if it is used with the same parameter for all internal nodes of the decision tree.
    Źródło:
    Journal of Medical Informatics & Technologies; 2011, 17; 65-71
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The dedicated decision support system in recognition of some uncertain disease entities
    Autorzy:
    Porwik, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/333041.pdf
    Data publikacji:
    2009
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    rozpoznawanie obrazu
    klasyfikacja danych
    sieci neuronowe
    systemy wspomagania decyzji
    image recognition
    data classification
    neural network
    decision support systems
    Opis:
    This work presents the principles of image recognition, where quality-based methods are applied. The neural networks and additional software have been proposed. This goal was achieved by using non-parametric recognition algorithms. In this paper the two-state hybrid classification method has been proposed, where artificial intelligence algorithm is included. In recognition process, the learning method, selection and optimization of diagnostic parameters have been introduced. The integrated part of the classifier structure is voting mechanism, which indicates incorrect states of the system – for example the unrecognized images. Effectiveness of the system has been shown by means of examples, where ambiguous data have been incorporated – it is very often a practice of medical diagnostics.
    Źródło:
    Journal of Medical Informatics & Technologies; 2009, 13; 97-100
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    User interaction simplification method for healthcare mobile applications
    Autorzy:
    Radliński, P.
    Sas, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/333657.pdf
    Data publikacji:
    2002
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    kliniczne systemy informatyczne
    systemy wspomagania podejmowania decyzji
    mobile computing
    clinical information systems
    decision making support systems
    Opis:
    In the paper an evaluation and optimisation framework for medical data access systems user interface is presented. User interface design seems to be of crucial importance for clinical data access applications acceptance, in particular for the applications running on handheld computers where the interface tools are significantly limited. The ease of program use depends strongly on the correct user interface design and on the algorithms which try to predict the user decisions and interactions made in the process of data access or modification. If the program is able to correctly predict the user actions and fetch him reasonable defaults then the number of interface actions which the user must do is significantly reduced. The method presented here focuses on typical functions available in clinical mobile data access systems: medication prescriptions and diagnostic and laboratory tests orders. The user interaction with an application is considered as the sequence of decisions. Using the records stored in the hospital database, the algorithm finds the most probable decisions at the subsequent stages of the interaction and uses it as defaults presented to the user. In this way instead of entering the data from the keyboard the user can much faster select it from the list.
    Źródło:
    Journal of Medical Informatics & Technologies; 2002, 4; MT111-118
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Handling class label noise in medical pattern classification systems
    Autorzy:
    Sáez, J. A.
    Krawczyk, B.
    Woźniak, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/333813.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    machine learning
    pattern classification
    class noise
    noise filtering
    decision support systems
    uczenie maszynowe
    klasyfikacja wzorców
    filtracja zakłóceń
    filtracja szumów
    systemy wspomagania decyzji
    Opis:
    Pattern classification systems play an important role in medical decision support. They allow to automatize and speed-up the data analysis process, while being able to handle complex and massive amounts of information and discover new knowledge. However, their quality is based on the classification models built, which require a training set. In supervised classification we must supply class labels to each training sample, which is usually done by domain experts or some automatic systems. As both of these approaches cannot be deemed as flawless, there is a chance that the dataset is corrupted by class noise. In such a situation, class labels are wrongly assigned to objects, which may negatively affect the classifier training process and impair the classification performance. In this contribution, we analyze the usefulness of existing tools to deal with class noise, known as noise filtering methods, in the context of medical pattern classification. The experiments carried out on several real-world medical datasets prove the importance of noise filtering as a pre-processing step and its beneficial influence on the obtained classification accuracy.
    Źródło:
    Journal of Medical Informatics & Technologies; 2015, 24; 123-130
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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