Distinction of Chaos from Randomness Is Not Possible from the Degree Distribution of the Visibility and Phase Space Reconstruction Graphs.

We investigate whether it is possible to distinguish chaotic time series from random time series using network theory. In this perspective, we selected four methods to generate graphs from time series: the natural, the horizontal, the limited penetrable horizontal visibility graph, and the phase space reconstruction method. These methods claim that the distinction of chaos from randomness is possible by studying the degree distribution of the generated graphs. We evaluated these methods by computing the results for chaotic time series from the 2D Torus Automorphisms, the chaotic Lorenz system, and a random sequence derived from the normal distribution. Although the results confirm previous studies, we found that the distinction of chaos from randomness is not generally possible in the context of the above methodologies.

Entropy and Network Centralities as Intelligent Tools for the Investigation of Terrorist Organizations.

In recent years, law enforcement authorities have increasingly used mathematical tools to support criminal investigations, such as those related to terrorism. In this work, two relevant questions are discussed: "How can the different roles of members of a terrorist organization be recognized?" and "are there early signs of impending terrorist acts?" These questions are addressed using the tools of entropy and network theory, more specifically centralities (degree, betweenness, clustering) and their entropies. These tools were applied to data (physical contacts) of four real terrorist networks from different countries. The different roles of the members are clearly recognized from the values of the selected centralities. An early sign of impending terrorist acts is the evolutionary pattern of the values of the entropies of the selected centralities. These results have been confirmed in all four terrorist networks. The conclusion is expected to be useful to law enforcement authorities to identify the roles of the members of terrorist organizations as the members with high centrality and to anticipate when a terrorist attack is imminent, by observing the evolution of the entropies of the centralities.

Semantic Representation of Patient Triage Data Collected in Emergency Departments.

Emergency Department (ED) overcrowding is a major issue for the efficient management of patients. To this end, triage algorithms have been developed to support the task of patient prioritization. In this paper an ontology was designed to represent the knowledge about patient triage procedure in EDs.

Enabling active and healthy ageing decision support systems with the smart collection of TV usage patterns.

Smart monitoring of seniors behavioural patterns and more specifically activities of daily living have attracted immense research interest in recent years. Development of smart decision support systems to support the promotion of health smart homes has also emerged taking advantage of the plethora of smart, inexpensive and unobtrusive monitoring sensors, devices and software tools. To this end, a smart monitoring system has been used in order to extract meaningful information about television (TV) usage patterns and subsequently associate them with clinical findings of experts. The smart TV operating state remote monitoring system was installed in four elderly women homes and gathered data for more than 11 months. Results suggest that TV daily usage (time the TV is turned on) can predict mental health change. Conclusively, the authors suggest that collection of smart device usage patterns could strengthen the inference capabilities of existing health DSSs applied in uncontrolled settings such as real senior homes.

Future perspectives toward the early definition of a multivariate decision-support scheme employed in clinical decision making for senior citizens.

Recent neuroscientific studies focused on the identification of pathological neurophysiological patterns (emotions, geriatric depression, memory impairment and sleep disturbances) through computerised clinical decision-support systems. Almost all these research attempts employed either resting-state condition (e.g. eyes-closed) or event-related potentials extracted during a cognitive task known to be affected by the disease under consideration. This Letter reviews existing data mining techniques and aims to enhance their robustness by proposing a holistic decision framework dealing with comorbidities and early symptoms' identification, while it could be applied in realistic occasions. Multivariate features are elicited and fused in order to be compared with average activities characteristic of each neuropathology group. A proposed model of the specific cognitive function which may be based on previous findings (a priori information) and/or validated by current experimental data should be then formed. So, the proposed scheme facilitates the early identification and prevention of neurodegenerative phenomena. Neurophysiological semantic annotation is hypothesised to enhance the importance of the proposed framework in facilitating the personalised healthcare of the information society and medical informatics research community.

Towards exergaming commons: composing the exergame ontology for publishing open game data.

BACKGROUND: It has been shown that exergames have multiple benefits for physical, mental and cognitive health. Only recently, however, researchers have started considering them as health monitoring tools, through collection and analysis of game metrics data. In light of this and initiatives like the Quantified Self, there is an emerging need to open the data produced by health games and their associated metrics in order for them to be evaluated by the research community in an attempt to quantify their potential health, cognitive and physiological benefits. METHODS: We have developed an ontology that describes exergames using the Web Ontology Language (OWL); it is available at http://purl.org/net/exergame/ns#. After an investigation of key components of exergames, relevant ontologies were incorporated, while necessary classes and properties were defined to model these components. A JavaScript framework was also developed in order to apply the ontology to online exergames. Finally, a SPARQL Endpoint is provided to enable open data access to potential clients through the web. RESULTS: Exergame components include details for players, game sessions, as well as, data produced during these game-playing sessions. The description of the game includes elements such as goals, game controllers and presentation hardware used; what is more, concepts from already existing ontologies are reused/repurposed. Game sessions include information related to the player, the date and venue where the game was played, as well as, the results/scores that were produced/achieved. These games are subsequently played by 14 users in multiple game sessions and the results derived from these sessions are published in a triplestore as open data. CONCLUSIONS: We model concepts related to exergames by providing a standardized structure for reference and comparison. This is the first work that publishes data from actual exergame sessions on the web, facilitating the integration and analysis of the data, while allowing open data access through the web in an effort to enable the concept of Open Trials for Active and Healthy Ageing.

Personal health records in the preclinical medical curriculum: modeling student responses in a simple educational environment utilizing Google Health.

BACKGROUND: Various problems concerning the introduction of personal health records in everyday healthcare practice are reported to be associated with physicians' unfamiliarity with systematic means of electronically collecting health information about their patients (e.g. electronic health records--EHRs). Such barriers may further prevent the role physicians have in their patient encounters and the influence they can have in accelerating and diffusing personal health records (PHRs) to the patient community. One way to address these problems is through medical education on PHRs in the context of EHR activities within the undergraduate medical curriculum and the medical informatics courses in specific. In this paper, the development of an educational PHR activity based on Google Health is reported. Moreover, student responses on PHR's use and utility are collected and presented. The collected responses are then modelled to relate the satisfaction level of students in such a setting to the estimation about their attitude towards PHRs in the future. METHODS: The study was conducted by designing an educational scenario about PHRs, which consisted of student instruction on Google Health as a model PHR and followed the guidelines of a protocol that was constructed for this purpose. This scenario was applied to a sample of 338 first-year undergraduate medical students. A questionnaire was distributed to each one of them in order to obtain Likert-like scale data on the sample's response with respect to the PHR that was used; the data were then further analysed descriptively and in terms of a regression analysis to model hypothesised correlations. RESULTS: Students displayed, in general, satisfaction about the core PHR functions they used and they were optimistic about using them in the future, as they evaluated quite high up the level of their utility. The aspect they valued most in the PHR was its main role as a record-keeping tool, while their main concern was related to the negative effect their own opinion might have on the use of PHRs by patients. Finally, the estimate of their future attitudes towards PHR integration was found positively dependent of the level of PHR satisfaction that they gained through their experience (rho=0.524, p<0.001). CONCLUSIONS: The results indicate that students support PHRs as medical record keeping helpers and perceive them as beneficial to healthcare. They also underline the importance of achieving good educational experiences in improving PHR perspectives inside such educational activities. Further research is obviously needed to establish the relative long-term effect of education to other methods of exposing future physicians to PHRs.

Dynamic composition of semantic pathways for medical computational problem solving by means of semantic rules.

This paper presents a semantic rule-based system for the composition of successful algorithmic pathways capable of solving medical computational problems (MCPs). A subset of medical algorithms referring to MCP solving concerns well-known medical problems and their computational algorithmic solutions. These solutions result from computations within mathematical models aiming to enhance healthcare quality via support for diagnosis and treatment automation, especially useful for educational purposes. Currently, there is a plethora of computational algorithms on the web, which pertain to MCPs and provide all computational facilities required to solve a medical problem. An inherent requirement for the successful construction of algorithmic pathways for managing real medical cases is the composition of a sequence of computational algorithms. The aim of this paper is to approach the composition of such pathways via the design of appropriate finite-state machines (FSMs), the use of ontologies, and SWRL semantic rules. The goal of semantic rules is to automatically associate different algorithms that are represented as different states of the FSM in order to result in a successful pathway. The rule-based approach is herein implemented on top of Knowledge-Based System for Intelligent Computational Search in Medicine (KnowBaSICS-M), an ontology-based system for MCP semantic management. Preliminary results have shown that the proposed system adequately produces algorithmic pathways in agreement with current international medical guidelines.

Toward emotion aware computing: an integrated approach using multichannel neurophysiological recordings and affective visual stimuli.

This paper proposes a methodology for the robust classification of neurophysiological data into four emotional states collected during passive viewing of emotional evocative pictures selected from the International Affective Picture System. The proposed classification model is formed according to the current neuroscience trends, since it adopts the independency of two emotional dimensions, namely arousal and valence, as dictated by the bidirectional emotion theory, whereas it is gender-specific. A two-step classification procedure is proposed for the discrimination of emotional states between EEG signals evoked by pleasant and unpleasant stimuli, which also vary in their arousal/intensity levels. The first classification level involves the arousal discrimination. The valence discrimination is then performed. The Mahalanobis (MD) distance-based classifier and support vector machines (SVMs) were used for the discrimination of emotions. The achieved overall classification rates were 79.5% and 81.3% for the MD and SVM, respectively, significantly higher than in previous studies. The robust classification of objective emotional measures is the first step toward numerous applications within the sphere of human-computer interaction.

On the classification of emotional biosignals evoked while viewing affective pictures: an integrated data-mining-based approach for healthcare applications.

Recent neuroscience findings demonstrate the fundamental role of emotion in the maintenance of physical and mental health. In the present study, a novel architecture is proposed for the robust discrimination of emotional physiological signals evoked upon viewing pictures selected from the International Affective Picture System (IAPS). Biosignals are multichannel recordings from both the central and the autonomic nervous systems. Following the bidirectional emotion theory model, IAPS pictures are rated along two dimensions, namely, their valence and arousal. Following this model, biosignals in this paper are initially differentiated according to their valence dimension by means of a data mining approach, which is the C4.5 decision tree algorithm. Then, the valence and the gender information serve as an input to a Mahalanobis distance classifier, which dissects the data into high and low arousing. Results are described in Extensible Markup Language (XML) format, thereby accounting for platform independency, easy interconnectivity, and information exchange. The average recognition (success) rate was 77.68% for the discrimination of four emotional states, differing both in their arousal and valence dimension. It is, therefore, envisaged that the proposed approach holds promise for the efficient discrimination of negative and positive emotions, and it is hereby discussed how future developments may be steered to serve for affective healthcare applications, such as the monitoring of the elderly or chronically ill people.

Towards a semantic framework for an integrative description of neuroscience patterns and studies: a case for emotion-related data.

The continuously increasing number of neuroscience studies and the difficulties associated with searching for related information and properly tracking neuroscience findings makes it imperative that one may be lead to isolated theories and findings which may be incompatible to each other or partially occluded. Semantically describing several aspects of studies in this field, such as, research groups attributes, aims of studies, experimental procedures followed, hardware and software tools utilised, acquisition systems used, as well as, the emerging neuro-physiological patterns found, may facilitate an integrative view of neuroscience theories. To this end, the current piece of work aims to provide a global theoretical framework using ontologies and semantic rules to describe neuroscience studies. Implementation details and applicability of the proof of concept are illustrated by means of an example targeting the semantic description of an emotion related study. The importance of the proposed framework in facilitating the envisaged personalised healthcare of the information society is discussed.

Ontology-based vector space model and fuzzy query expansion to retrieve knowledge on medical computational problem solutions.

Medical Computational Problem (MCP) solving is related to medical problems and their computerized algorithmic solutions. In this paper, an extension of an ontology-based model to fuzzy logic is presented, as a means to enhance the information retrieval (IR) procedure in semantic management of MCPs. We present herein the methodology followed for the fuzzy expansion of the ontology model, the fuzzy query expansion procedure, as well as an appropriate ontology-based Vector Space Model (VSM) that was constructed for efficient mapping of user-defined MCP search criteria and MCP acquired knowledge. The relevant fuzzy thesaurus is constructed by calculating the simultaneous occurrences of terms and the term-to-term similarities derived from the ontology that utilizes UMLS (Unified Medical Language System) concepts by using Concept Unique Identifiers (CUI), synonyms, semantic types, and broader-narrower relationships for fuzzy query expansion. The current approach constitutes a sophisticated advance for effective, semantics-based MCP-related IR.

KnowBaSICS-M: an ontology-based system for semantic management of medical problems and computerised algorithmic solutions.

In this paper, an ontology-based system (KnowBaSICS-M) is presented for the semantic management of Medical Computational Problems (MCPs), i.e., medical problems and computerised algorithmic solutions. The system provides an open environment, which: (1) allows clinicians and researchers to retrieve potential algorithmic solutions pertinent to a medical problem and (2) enables incorporation of new MCPs into its underlying Knowledge Base (KB). KnowBaSICS-M is a modular system for MCP acquisition and discovery that relies on an innovative ontology-based model incorporating concepts from the Unified Medical Language System (UMLS). Information retrieval (IR) is based on an ontology-based Vector Space Model (VSM) that estimates the similarity among user-defined MCP search criteria and registered MCP solutions in the KB. The results of a preliminary evaluation and specific examples of use are presented to illustrate the benefits of the system. KnowBaSICS-M constitutes an approach towards the construction of an integrated and manageable MCP repository for the biomedical research community.