Applying clustering based on rules on WHO-DAS II for knowledge discovery on functional disabilities

The senior citizens represent a fast growing proportion of the population in Europe and other developed areas. This increases the proportion of persons with disability and reducing quality of life. The concept of disability itself is not always precise and quantifiable. To improve agreement on the concept of disability, the World Health Organization (WHO) developed a clinical test WHO Disability Assessment Schedule, (WHO-DASII) that is understood to include physical, mental, and social well-being, as a generic measure of functioning. From the medical point of view, the purpose of this work is to extract knowledge on the performance of the test WHO-DASII on the basis of a sample of neurological patients from an Italian hospital. This Knowledge Discovery problem has been faced by using clustering based on rules, a technique stablished on 1994 by Gibert which combines some Inductive Learning (from AI) methods with Statistics to extract knowledge on ill-structured domains (that is complex domains where consensus is not achieved, like is the case). So, in this paper, the results of applying this technique to the WHO-DASII results is presented.Postprint (published version

Toward Optimization of Medical Therapies with a Little Help from Knowledge Management

This chapter emphasizes the importance of identifying and managing knowledge from Informally Structured Domains, especially in the medical field, where very short and repeated serial measurements are often present. This information is made up of attributes of both patients and their treatments that influence their state of health and usually includes measurements of various parameters taken at different times during the duration of treatment and usually after the application of the therapeutic resource. The chapter communicates the use of the KDSM methodology through a case study and the importance of paying attention to the characteristics of the domain to perform appropriate knowledge management in the domain

The role of inflammation in age-related disease.

The National Institutes of Health (NIH) Geroscience Interest Group (GSIG) sponsored workshop, The Role of Inflammation inAge-Related Disease, was held September 6th-7th, 2012 in Bethesda, MD. It is now recognized that a mild pro-inflammatory state is correlated with the major degenerative diseases of the elderly. The focus of the workshop was to better understand the origins and consequences of this low level chronic inflammation in order to design appropriate interventional studies aimed at improving healthspan. Four sessions explored the intrinsic, environmental exposures and immune pathways by which chronic inflammation are generated, sustained, and lead to age-associated diseases. At the conclusion of the workshop recommendations to accelerate progress toward understanding the mechanistic bases of chronic disease were identified

End-to-End Entity Resolution for Big Data: A Survey

One of the most important tasks for improving data quality and the reliability of data analytics results is Entity Resolution (ER). ER aims to identify different descriptions that refer to the same real-world entity, and remains a challenging problem. While previous works have studied specific aspects of ER (and mostly in traditional settings), in this survey, we provide for the first time an end-to-end view of modern ER workflows, and of the novel aspects of entity indexing and matching methods in order to cope with more than one of the Big Data characteristics simultaneously. We present the basic concepts, processing steps and execution strategies that have been proposed by different communities, i.e., database, semantic Web and machine learning, in order to cope with the loose structuredness, extreme diversity, high speed and large scale of entity descriptions used by real-world applications. Finally, we provide a synthetic discussion of the existing approaches, and conclude with a detailed presentation of open research directions

Subseasonal Temporal Clustering of Extreme Precipitation in the Northern Hemisphere: Regionalization and Physical Drivers

Temporal clustering of extreme precipitation (TCEP) at subseasonal time scales often results in major impactson humans and ecosystems. Assessment and mitigation of the risk of such events requires characterization of their weather/climate drivers and their spatial dependence. Here, we introduce a regionalization method that identifies coherent regions in which the likelihood of subseasonal TCEP exhibits similar dependence to large-scale dynamics. We apply this method to each season in the Northern Hemisphere using ERA5 reanalysis data. The analysis yields spatially coherent regions, primarily at high latitudes and along the eastern margins of ocean basins. We analyze the large-scale and synoptic conditions associated with TCEP in several of the identified regions, in light of three key ingredients: lifting, moisture availability, and persistence in synoptic conditions. We find that TCEP is often directly related to distinct cyclone and blocking frequency anomalies and upper-level wave patterns. Blocking and associated Rossby wave breaking are particularly relevant at high latitudes and midlatitudes. At upper levels, meridional wave patterns dominate; however, in western Europe and parts of North America, TCEP is sometimes associated with zonally extended wave patterns. The flow features associated with TCEP in the eastern Pacific and eastern Atlantic Oceans exhibit similarities. For some regions, moisture flux anomalies are present during clustering episodes whereas in others forced lifting alone is sufficient to trigger heavy precipitation. Our results provide new information on the dynamics and spatial dependence of TCEP that may be relevant for the subseasonal prediction of clustering episodes

Dynamic production system identification for smart manufacturing systems

This paper presents a methodology, called production system identification, to produce a model of a manufacturing system from logs of the system's operation. The model produced is intended to aid in making production scheduling decisions. Production system identification is similar to machine-learning methods of process mining in that they both use logs of operations. However, process mining falls short of addressing important requirements; process mining does not (1) account for infrequent exceptional events that may provide insight into system capabilities and reliability, (2) offer means to validate the model relative to an understanding of causes, and (3) updated the model as the situation on the production floor changes. The paper describes a genetic programming (GP) methodology that uses Petri nets, probabilistic neural nets, and a causal model of production system dynamics to address these shortcomings. A coloured Petri net formalism appropriate to GP is developed and used to interpret the log. Interpreted logs provide a relation between Petri net states and exceptional system states that can be learned by means of novel formulation of probabilistic neural nets (PNNs). A generalized stochastic Petri net and the PNNs are used to validate the GP-generated solutions. The methodology is evaluated with an example based on an automotive assembly system

The Impact of glucose and glucoregulation on memory

The effect of glucose on memory has been investigated for in excess of 25 years, with some consensus generated amongst the literature indicating that glucose has a facilitating effect. However, the robustness of the glucose effect has been questioned, with a considerable body of evidence reporting no glucose facilitation of memory. It has been suggested that glucoregulatory control may be a key mediating factor of the glucose effect. Glucoregulatory control and cognitive functioning are intrinsically linked, with cognitive impairments a common feature in populations presenting with poor glucoregulatory control such as diabetics, Alzheimer‘s disease sufferers, schizophrenics and the elderly. Although again the evidence has proven contradictory, with evidence to suggest that both better and poorer glucoregulators are more / less susceptible to the glucose effects on cognition. Verbal declarative memory has been reported to be the most reliably enhanced aspect of memory to benefit from a glucose effect. However, it is not yet clear whether verbal declarative memory as a whole is being facilitated, or whether the different phases of memory (encoding, consolidation, retrieval etc.) are differentially targeted. Consequently the primary aim of this thesis was to evaluate the effect of glucoregulatory control and glucose, on the different phases of verbal declarative memory. This was achieved through the use of novel paradigms employed previously within the cognitive sciences literature. Chapter 2 addressed a secondary aim of this thesis; investigating the current gap in the literature pertaining to the effect of glucose administration on cognition in children. Chapter 3 investigated the types of recognition (recollection and familiarity) that were made subsequent to a glucose load, using the ?remember/know‘ paradigm. Chapter 4 investigated encoding efficiency during the item method directed forgetting paradigm, in which participants actively attempt to forget specific stimuli through cessation of encoding. In chapters 5 and 6 the potential mediation of inhibition processes was explored, with both semantically related (Retrieval Induced Forgetting paradigm) and orthographically similar but semantically unrelated stimuli (Memory Blocking Effect paradigm). The tentative evidence presented in this thesis indicates that glucoregulatory control may mediate the glucose facilitation effect during the encoding phase, with better regulators seemingly benefiting from greater encoding benefits than poorer following glucose. Glucose was not observed to influence inhibition processes, or types of recognitions made. However, better glucoregulators exhibited more efficient adaptive inhibition (overcoming inhibition of blocking items to continue searching the lexicon and increased inhibition of semantically related competing stimuli). Administration of glucose did not mediate cognition in children, with the exception of an impairment of performance on a challenging reaction time task following 20 g of glucose. Memory phases are seemingly differentially affected by glucose administration, with the effect mediated by glucoregulatory control. Utilising the paradigms employed here (or similar) to investigate a range of populations presenting with cognitive decline / glucoregulatory control, would further allow the glucose and glucoregulatory effects on the different phases of memory to be further disentangled

Parallel processing of semantics and phonology in spoken production:Evidence from blocked cyclic picture naming and EEG

Spoken language production involves lexical-semantic access and phonological encoding. A theoretically important question concerns the relative time course of these two cognitive processes. The predominant view has been that semantic and phonological codes are accessed in successive stages. However, recent evidence seems difficult to reconcile with a sequential view but rather suggests that both types of codes are accessed in parallel. Here, we used ERPs combined with the "blocked cyclic naming paradigm" in which items overlapped either semantically or phonologically. Behaviorally, both semantic and phonological overlap caused interference relative to unrelated baseline conditions. Crucially, ERP data demonstrated that the semantic and phonological effects emerged at a similar latency (similar to 180 msec after picture onset) and within a similar time window (180-380 msec). These findings suggest that access to phonological information takes place at a relatively early stage during spoken planning, largely in parallel with semantic processing

Weather persistence on sub-seasonal to seasonal timescales: a methodological review

Persistence is an important concept in meteorology. It refers to surface weather or the atmospheric circulation either remaining in approximately the same state (quasi-stationarity) or repeatedly occupying the same state (recurrence) over some prolonged period of time. Persistence can be found at many different timescales; however, sub-seasonal to seasonal (S2S) timescales are especially relevant in terms of impacts and atmospheric predictability. For these reasons, S2S persistence has been attracting increasing attention from the scientific community. The dynamics responsible for persistence and their potential evolution under climate change are a notable focus of active research. However, one important challenge facing the community is how to define persistence from both a qualitative and quantitative perspective. Despite a general agreement on the concept, many different definitions and perspectives have been proposed over the years, among which it is not always easy to find one's way. The purpose of this review is to present and discuss existing concepts of weather persistence, associated methodologies and physical interpretations. In particular, we call attention to the fact that persistence can be defined as a global or as a local property of a system, with important implications in terms of methods and impacts. We also highlight the importance of timescale and similarity metric selection and illustrate some of the concepts using the example of summertime atmospheric circulation over western Europe