8 research outputs found

    Artificial Intelligence Methods for Modelling Tremor Mechanisms

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    Tremors are one of the most common movement disorders primarily associated with various neurological diseases. Since there are more than 20 different types of tremors, differentiation between them is important from the treatment point of view. In the thesis, we focus on differentiation between three of the most common tremors: Parkinsonian, essential and mixed type of tremor. Our first goal was to build a diagnostic model for distinguishing between Parkinsonian, essential and mixed type of tremors, based on clinical examination data, family history and digital spirography. The process of building a model was carried out using argument-based machine learning which enabled us to build a decision model through the process of knowledge elicitation from the domain expert (in our case from a neurologist). The obtained model consists of thirteen rules that are medically sensible. The process of knowledge elicitation itself contributed to the higher classification accuracy of the final model in comparison with the initial one. In the final diagnostic model, attributes derived from the spirography were included in more than half of the rules. This motivated us to build a model based solely on the digital spirography data. For the needs of constructing an understandable model, we first built several attributes which represented domain medical knowledge. We have built more than 500 different attributes which were used in a logistic regression to construct the final diagnostic model. The model is able to distinguish subjects with tremors from those without tremors with 90% classification accuracy. During the process of attribute construction, we wanted to know what our attributes were detecting. Thus, we have developed a method for attribute visualisation on series. The method not only helped us with attribute construction, but it is also useful for visual interpretation of the diagnostic model's decisions. The visualisation method and consequently the decision model were evaluated with the help of three independent neurology experts. The results show that both the diagnostic model and the visualisation are meaningful and cover medical knowledge of the domain. The final diagnostic model is built into the freely available ParkinsonCheck mobile application

    Artificial Intelligence Methods for Modelling Tremor Mechanisms

    Get PDF
    Tremors are one of the most common movement disorders primarily associated with various neurological diseases. Since there are more than 20 different types of tremors, differentiation between them is important from the treatment point of view. In the thesis, we focus on differentiation between three of the most common tremors: Parkinsonian, essential and mixed type of tremor. Our first goal was to build a diagnostic model for distinguishing between Parkinsonian, essential and mixed type of tremors, based on clinical examination data, family history and digital spirography. The process of building a model was carried out using argument-based machine learning which enabled us to build a decision model through the process of knowledge elicitation from the domain expert (in our case from a neurologist). The obtained model consists of thirteen rules that are medically sensible. The process of knowledge elicitation itself contributed to the higher classification accuracy of the final model in comparison with the initial one. In the final diagnostic model, attributes derived from the spirography were included in more than half of the rules. This motivated us to build a model based solely on the digital spirography data. For the needs of constructing an understandable model, we first built several attributes which represented domain medical knowledge. We have built more than 500 different attributes which were used in a logistic regression to construct the final diagnostic model. The model is able to distinguish subjects with tremors from those without tremors with 90% classification accuracy. During the process of attribute construction, we wanted to know what our attributes were detecting. Thus, we have developed a method for attribute visualisation on series. The method not only helped us with attribute construction, but it is also useful for visual interpretation of the diagnostic model's decisions. The visualisation method and consequently the decision model were evaluated with the help of three independent neurology experts. The results show that both the diagnostic model and the visualisation are meaningful and cover medical knowledge of the domain. The final diagnostic model is built into the freely available ParkinsonCheck mobile application

    Argument-based machine learning with logistic regression

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    People nowadays tend to use simple tools and procedures to process and analyze new data. We are eager to find easy solutions to extract useful information from it and build predictive models. In this thesis we designed a tool, which can easily be used to cope with a new data and which also enables a possibility to articulate expert's domain knowledge in the form of new features, i.e. attributes. The tool is based on a paradigm of argument-based machine learning (ABML) and machine learning method, called logistic regression. We modified the logistic regression method by adding the possibility to articulate the expert's domain knowledge and developed a new method, that allows interaction between domain expert and logistic regression called argument-based machine learning with logistic regression. We created an application with a graphical user interface that uses newly created method and, by using interactive loop, captures domain expert's knowledge. The knowledge is passed into the predictive model in the form of new attributes. Method searches for problematic examples which are examples that are wrongly predicted by a logistic regression model. These examples are presented to the domain expert. Expert's task is now to explain critical examples by giving arguments and providing explanations for wrongly predicted example. According to the given expert's arguments, method finds relevant couterexamples which can highlight possible flaws and shortcomings in the expert's arguments. Counterexamples change regulary, based on the conditions mentioned by the expert. The interaction between the expert and argument-based machine learning method can lead to better and more accurate models that are consistent with expert's domain knowledge. The newly created application also enables creating new attributes, which can be made during the argumentation process. If the newly created attribute solves current critical example, it gets replaced by a new problematic example. This leads to a faster interaction between a domain expert and the machine learning algorithm

    Development of graphical user interface for argument-based intelligent tutoring system

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    Graphical user interface, GUI, today, constitutes an important part of a computer system. They can be found almost everywhere, whether in a cell phone, tablet, computer, machine, car, watch or control panel in a power plant, to name a few. The massive role of the internet has increased the popularity of web GUI's. Via the internet we can use a variety of online portals and applications, for e.g. even to control smart houses. Due to their simplicity they can be used by anyone, in most cases, without having special knowledge. The task of this thesis was to create the graphical interface for an existing, reasoning-based intelligent tutoring system. It was important that the GUI was consistent with the mode of use of the intelligent tutoring system, ITS, which, until now, operated only as a console application. ITS uses a large amount of data in its operation, therefore, upon implementation of these in the graphical user interface, it is most important that they are properly structured, as only then can they come to the fore. The first challenge was therefore the data display system. Here, we had to bear in mind that the intelligence system running in the background also supported the work of different data for a variety of didactic domains. Another important challenge was related to communication between the intelligent tutoring system and the web server, on which the GUI is located. In addressing these challenges we used several different methods. To assist in planning, we used various principles found both in the literature and on the web. We created a simple and clear graphical interface, based on a one-sided page view. This enabled a dynamic display and optimised the display loading time. With an appropriate selection of colours, we contrived an attractive appearance for the interface and the correct setting up of elements, that all together brought a good user-experience. We connected the graphical interface using a socket, enabling real-time communication. The developed web application, among other things, allows the teacher to determine the advanced concepts in the selected didactic domain, upon which the student will be specially focused when explaining learning examples. The GUI, then gives a continual display of the progress of the student in these selected concepts. The operation of the graphical user interface was demonstrated in didactic domain, where students learn how to understand and establish company credit ratings

    Development of graphical user interface for argument-based intelligent tutoring system

    Get PDF
    Graphical user interface, GUI, today, constitutes an important part of a computer system. They can be found almost everywhere, whether in a cell phone, tablet, computer, machine, car, watch or control panel in a power plant, to name a few. The massive role of the internet has increased the popularity of web GUI's. Via the internet we can use a variety of online portals and applications, for e.g. even to control smart houses. Due to their simplicity they can be used by anyone, in most cases, without having special knowledge. The task of this thesis was to create the graphical interface for an existing, reasoning-based intelligent tutoring system. It was important that the GUI was consistent with the mode of use of the intelligent tutoring system, ITS, which, until now, operated only as a console application. ITS uses a large amount of data in its operation, therefore, upon implementation of these in the graphical user interface, it is most important that they are properly structured, as only then can they come to the fore. The first challenge was therefore the data display system. Here, we had to bear in mind that the intelligence system running in the background also supported the work of different data for a variety of didactic domains. Another important challenge was related to communication between the intelligent tutoring system and the web server, on which the GUI is located. In addressing these challenges we used several different methods. To assist in planning, we used various principles found both in the literature and on the web. We created a simple and clear graphical interface, based on a one-sided page view. This enabled a dynamic display and optimised the display loading time. With an appropriate selection of colours, we contrived an attractive appearance for the interface and the correct setting up of elements, that all together brought a good user-experience. We connected the graphical interface using a socket, enabling real-time communication. The developed web application, among other things, allows the teacher to determine the advanced concepts in the selected didactic domain, upon which the student will be specially focused when explaining learning examples. The GUI, then gives a continual display of the progress of the student in these selected concepts. The operation of the graphical user interface was demonstrated in didactic domain, where students learn how to understand and establish company credit ratings

    Argumentation for machine learning: a survey

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    Existing approaches using argumentation to aid or improve machine learning differ in the type of machine learning technique they consider, in their use of argumentation and in their choice of argumentation framework and semantics. This paper presents a survey of this relatively young field highlighting, in particular, its achievements to date, the applications it has been used for as well as the benefits brought about by the use of argumentation, with an eye towards its future

    Estimating the quality of arguments in argument-based machine learning

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    Argument-based machine learning (ABML) knowledge refinement loop enables an interaction between a machine learning algorithm and a domain expert. It represents a powerful knowledge elicitation tool, suitable for obtaining expert knowledge in difficult domains. The loop enables the expert to focus on the most critical parts of the current knowledge base, and helps him or her to argue about automatically chosen relevant examples. The expert only needs to explain a single example at the time, which facilitates articulating arguments. It also helps the expert to improve the explanations by providing (automatically chosen) relevant counter examples. It has been shown recently that ABML knowledge refinement loop also enables design of argumentation-based interactive teaching tool. However, so far the machine was not able to provide neither the teachers (that designed such a tool) nor the students (that used it for learning) with concrete estimations about the quality of their arguments. In this thesis, we have designed three approaches for giving immediate feedback about the quality of arguments used in the ABML knowledge refinement loop. The chosen experimental domain was financial statement analysis, more concretely estimating credit scores of companies (enterprises). Our goal was twofold: to obtain a successful classification model for predicting the credit scores, and to enable the students to learn about this rather difficult domain. In the experimental sessions, both the teacher and the students were involved in the process of knowledge elicitation with the ABML knowledge refinement loop, receiving the feedback about their arguments. The goal of the learning session with the teacher was in particular to obtain advanced concepts (attributes) that describe the domain well, are suitable for teaching, and also enable successful predictions. This was done with the help of a financial expert. In the “tutoring" sessions, the students learned about the intricacies of the domain and strived for the best predictive model as possible, also by using the teacher's advanced concepts in their arguments. The main contributions of this work are: - the design of three approaches for estimating the quality of arguments used in the argument-based machine learning (ABML) knowledge refinement loop, - implementation of argumentation-based interactive teaching tool for estimating credit scores of companies (enterprises), using real data, - a detailed description of the learning session, where the student received three types of feedback about the arguments used

    Estimating the quality of arguments in argument-based machine learning

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    Argumentirano strojno učenje (angl. argument-based machine learning, ABML) omogoča interakcijo med metodo strojnega učenja in ekspertom v izbrani domeni ter z njo elicitacijo znanja iz domenskega eksperta. Ekspert pojasni samo skrbno izbrane kritične" primere in tako na hiter in učinkovit način podaja le relevantno znanje. ABML lahko uporabimo tudi kot inteligentni sistem za poučevanje, temelječem na argumentiranju. S podajanjem povratne informacije o kvaliteti podanega argumenta lahko efektivnost podajanja znanja še povečamo. V delu smo zasnovali in implementirali 2 meri za ocenjevanje argumentov. Evalvacijo mer (2 novi, 1 obstoječa) smo izvedli v sklopu ABML postopka pri gradnji napovednega modela za napovedovanje bonitetnih ocen podjetjem. Eksperiment je vseboval dva dela, elicitacijo znanja iz učitelja in elicitacijo znanja iz učenca. V prvem delu s pomočjo finančnega eksperta dosežemo konsistenten nabor podatkov in uvedbo naprednejših konceptov, ki opisujejo domeno. Drugi del predstavlja učno sejo, v kateri se učenec spozna z domeno in nauči razumevanja konceptov preko interaktivne učne zanke. V izvedbi postopka z učenci se je ena izmed razvitih mer izkazala za posebej uspešno.Argument-based machine learning (ABML) enables an interaction between a machine learning algorithm and an expert in a given domain, in order to achieve successful knowledge elicitation from the domain expert. The expert provides knowledge in a quick and efficient way by explaining only automatically chosen critical" examples. ABML can also be used as an argumentation-based teaching tool. By providing more information about the quality of the given arguments, we can improve the effectiveness of the knowledge elicitation. In our thesis, we have designed and implemented two measures for estimating the quality of arguments. Evaluation of measures (2 new, 1 existent) was done through an ABML procedure, where we learned a classification model for predicting the credit score of companies. Experiment consisted of two parts: knowledge elicitation from the teacher, and knowledge elicitation from the student. The goal of the first part was to obtain a consistent data set and introduction of advanced concepts, that describe the domain. This was done with the help of a financial expert. The second part was the tutoring session, where the student learned the intricacies of the domain and achieved comprehension of the advanced concepts, by means of using the interactive tutoring loop. While carrying out the teaching trials with the students, one measure proved to be particularly successful
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