Data Mining in Clinical Practice for the Quantification of Motor Impairment in Parkinson's Disease

Advances in biomedical signal acquisition systems for motion analysis have led to lowcost and ubiquitous wearable sensors which can be used to record movement data in different settings. This implies the potential availability of large amounts of quantitative data. It is then crucial to identify and to extract the information of clinical relevance from the large amount of available data. This quantitative and objective information can be an important aid for clinical decision making. Data mining is the process of discovering such information in databases through data processing, selection of informative data, and identification of relevant patterns. The databases considered in this thesis store motion data from wearable sensors (specifically accelerometers) and clinical information (clinical data, scores, tests). The main goal of this thesis is to develop data mining tools which can provide quantitative information to the clinician in the field of movement disorders. This thesis will focus on motor impairment in Parkinson's disease (PD). Different databases related to Parkinson subjects in different stages of the disease were considered for this thesis. Each database is characterized by the data recorded during a specific motor task performed by different groups of subjects. The data mining techniques that were used in this thesis are feature selection (a technique which was used to find relevant information and to discard useless or redundant data), classification, clustering, and regression. The aims were to identify high risk subjects for PD, characterize the differences between early PD subjects and healthy ones, characterize PD subtypes and automatically assess the severity of symptoms in the home setting.

Applicability of Process Mining Techniques in Business Environments

This thesis analyses problems related to the applicability, in business environments, of Process Mining tools and techniques. The first contribution is a presentation of the state of the art of Process Mining and a characterization of companies, in terms of their "process awareness". The work continues identifying circumstance where problems can emerge: data preparation; actual mining; and results interpretation. Other problems are the configuration of parameters by not-expert users and computational complexity. We concentrate on two possible scenarios: "batch" and "on-line" Process Mining. Concerning the batch Process Mining, we first investigated the data preparation problem and we proposed a solution for the identification of the "case-ids" whenever this field is not explicitly indicated. After that, we concentrated on problems at mining time and we propose the generalization of a well-known control-flow discovery algorithm in order to exploit non instantaneous events. The usage of interval-based recording leads to an important improvement of performance. Later on, we report our work on the parameters configuration for not-expert users. We present two approaches to select the "best" parameters configuration: one is completely autonomous; the other requires human interaction to navigate a hierarchy of candidate models. Concerning the data interpretation and results evaluation, we propose two metrics: a model-to-model and a model-to-log. Finally, we present an automatic approach for the extension of a control-flow model with social information, in order to simplify the analysis of these perspectives. The second part of this thesis deals with control-flow discovery algorithms in on-line settings. We propose a formal definition of the problem, and two baseline approaches. The actual mining algorithms proposed are two: the first is the adaptation, to the control-flow discovery problem, of a frequency counting algorithm; the second constitutes a framework of models which can be used for different kinds of streams (stationary versus evolving).

Development of glass-ceramics from combination of industrial wastes together with boron mining waste

The utilization of borate mineral wastes with glass-ceramic technology was first time studied and primarily not investigated combinations of wastes were incorporated into the research. These wastes consist of; soda lime silica glass, meat bone and meal ash and fly ash. In order to investigate possible and relevant application areas in ceramics, kaolin clay, an essential raw material for ceramic industry was also employed in some studied compositions. As a result, three different glass-ceramic articles obtained by using powder sintering method via individual sintering processes. Light weight micro porous glass-ceramic from borate mining waste, meat bone and meal ash and kaolin clay was developed. In some compositions in related study, soda lime silica glass waste was used as an additive providing lightweight structure with a density below 0.45 g/cm3 and a crushing strength of 1.8±0.1 MPa. In another study within the research, compositions respecting the B2O3–P2O5–SiO2 glass-ceramic ternary system were prepared from; borate wastes, meat bone and meal ash and soda lime silica glass waste and sintered up to 950ºC. Low porous, highly crystallized glass-ceramic structures with density ranging between 1.8 ± 0,7 to 2.0 ± 0,3 g/cm3 and tensile strength ranging between 8,0 ± 2 to 15,0 ± 0,5 MPa were achieved. Lastly, diopside - wollastonite (SiO2-Al2O3-CaO )glass-ceramics from borate wastes, fly ash and soda lime silica glass waste were successfully obtained with controlled rapid sintering between 950 and 1050ºC. The wollastonite and diopside crystal sizes were improved by adopting varied combinations of formulations and heating rates. The properties of the obtained materials show; the articles with a uniform pore structure could be useful for thermal and acoustic insulations and can be embedded in lightweight concrete where low porous glass-ceramics can be employed as building blocks or additive in cement and ceramic industries.