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.

Self-Organizing Mechanisms for Task Allocation in a Knowledge-Based Economy

A prevalent claim is that we are in knowledge economy. When we talk about knowledge economy, we generally mean the concept of “Knowledge-based economy” indicating the use of knowledge and technologies to produce economic benefits. Hence knowledge is both tool and raw material (people’s skill) for producing some kind of product or service. In this kind of environment economic organization is undergoing several changes. For example authority relations are less important, legal and ownership-based definitions of the boundaries of the firm are becoming irrelevant and there are only few constraints on the set of coordination mechanisms. Hence what characterises a knowledge economy is the growing importance of human capital in productive processes (Foss, 2005) and the increasing knowledge intensity of jobs (Hodgson, 1999). Economic processes are also highly intertwined with social processes: they are likely to be informal and reciprocal rather than formal and negotiated. Another important point is also the problem of the division of labor: as economic activity becomes mainly intellectual and requires the integration of specific and idiosyncratic skills, the task of dividing the job and assigning it to the most appropriate individuals becomes arduous, a “supervisory problem” (Hogdson, 1999) emerges and traditional hierarchical control may result increasingly ineffective. Not only specificity of know how makes it awkward to monitor the execution of tasks, more importantly, top-down integration of skills may be difficult because ‘the nominal supervisors will not know the best way of doing the job – or even the precise purpose of the specialist job itself – and the worker will know better’ (Hogdson,1999). We, therefore, expect that the organization of the economic activity of specialists should be, at least partially, self-organized. The aim of this thesis is to bridge studies from computer science and in particular from Peer-to-Peer Networks (P2P) to organization theories. We think that the P2P paradigm well fits with organization problems related to all those situation in which a central authority is not possible. We believe that P2P Networks show a number of characteristics similar to firms working in a knowledge-based economy and hence that the methodology used for studying P2P Networks can be applied to organization studies. Three are the main characteristics we think P2P have in common with firms involved in knowledge economy: - Decentralization: in a pure P2P system every peer is an equal participant, there is no central authority governing the actions of the single peers; - Cost of ownership: P2P computing implies shared ownership reducing the cost of owing the systems and the content, and the cost of maintaining them; - Self-Organization: it refers to the process in a system leading to the emergence of global order within the system without the presence of another system dictating this order. These characteristics are present also in the kind of firm that we try to address and that’ why we have shifted the techniques we adopted for studies in computer science (Marcozzi et al., 2005; Hales et al., 2007 [39]) to management science.

Role of new molecular approaches for the early diagnosis of bladder cancer in clinical practice

There is an urgent need to improve the performance of urine cytology for the diagnosis of bladder cancer. In preliminary studies, telomerase activity evaluated by telomeric repeat amplification protocol (TRAP) assay and chromosomal aneuploidy detected by fluorescence in situ hybridization (FISH) in the diagnosis of bladder cancer have produced important results. Urine cell-free (UCF) DNA has also been proposed as a potential marker for early bladder cancer diagnosis. In the first study the diagnostic performance of TRAP assay and FISH analysis was assessed, while the second study evaluated the potential role of UCF DNA integrity in early bladder cancer diagnosis. In the first cross-sectional study, 289 consecutive patients who presented with urinary symptoms underwent cystoscopy and cytology evaluation. In the second study, UCF DNA was isolated from 51 bladder cancer patients, 46 symptomatic patients, and 32 healthy volunteers. c-Myc, BCAS1 and HER2 gene sequences longer than 250 bp were quantified by real time PCR to verify UCF DNA integrity. In the first study, sensitivity and specificity were 0.39 and 0.83, respectively, for cytology; 0.66 and 0.72 for TRAP; 0.78 and 0.60 for the cytology and TRAP combination; 0.78 and 0.78 for the cytology, TRAP and FISH combination; and 0.65 and 0.93 for the TRAP and FISH combination. In the second study, at the best cutoff of 0.1 ng/µl, UCF DNA integrity analysis showed a sensitivity of 0.73 and a specificity of 0.84 in healthy individuals and 0.83 in symptomatic patients. The preliminary results suggest that these biomarkers could potentially be used for the early diagnosis of bladder cancer, especially in high-risk populations (e.g, symptomatic individuals exposed to occupational risk) who may benefit from the use of noninvasive diagnostic tests in terms of cost-benefit.