Mining of patient data: towards better treatment strategies for depression

An intelligent system based on data-mining technologies that can be used to assist in the prevention and treatment of depression is described. The system integrates three different kinds of patient data as well as the data describing mental health of therapists and their interaction with the patients. The system allows for the different data to be analysed in a conjoint manner using both traditional data-mining techniques and tree-mining techniques. Interesting patterns can emerge in this way to explain various processes and dynamics involved in the onset, treatment and management of depression, and help practitioners develop better prevention and treatment strategies

WiBACK: A back-haul network architecture for 5G networks

Recently both academic and industry worlds has started to define the successor of Long Term Evolution (LTE), so-called 5G networks, which will most likely appear by the end of the decade. It is widely accepted that those 5G networks will have to deal with significantly more challenging requirements in terms of provided bandwidth, latency and supported services. This will lead to not only modifications in access and parts of core networks, but will trigger changes throughout the whole network, including the Back-haul segment. In this work we present our vision of a 5G Back-haul network and identify the associated challenges. We then describe our Wireless Backhaul (WiBACK) architecture, which implements Software Defined Network (SDN) concepts and further extends them into the wireless domain. Finally we present a brief overview of our pilot installations before we conclude.This work has been supported by the BATS research project which is funded by the European Union Seventh Framework Programme under contract n317533

Thinking PubMed: an innovative system for mental health domain

Information regarding mental illness is dispersed over various resources but even within a specific resource, such as PubMed, it is difficult to link this information, to share it and find specific information when needed. Specific and targeted searches are very difficult with current search engines as they look for the specific string of letters within the text rather than its meaning.In this paper we present Thinking PubMed as a system that results from synergy of ontology and data mining technologies and performs intelligent information searches using the domain ontology. Furthermore, the Thinking PubMed analyzes and links the retrieved information, and extracts hidden patterns and knowledge using data mining algorithms. This is a new generation of information-seeking tool where the ontology and data-mining work in concert to increase the value of the available information

Application of digital ecosystems in health domain

Digital Ecosystems (DES) have recently been introduced into the computer and information societies. A Digital Ecosystem is the dynamic and synergetic complex of Digital Communities consisting of interconnected, interrelated and interdependent Digital Species situated in a Digital Environment, that intereact as a functional unit and are linked together through actions, information and transaction flows. Digital Ecosystems integrate various cutting-edge technologies including ontologies, agent-based and self-organizing systems, swarm intelligence, ambient intelligence, data mining etc. The synergetic effects of these methodologies results in a more efficient, effective, reliable and secure system.The application of DES within the health domain would transform the way in which health information is created, stored, accessed, used, managed, analyzed and shared, and would bring an innovative breakthrough within bealth domain. In this paper, we illustrate how the DES Design Methodology can be implemented within the health domain. We focus on the key factors associated with the DES design. The design methodology framework allows better control over the design process and serves as a navigating tool during the Digital Health Ecosystems design

Using the symmetrical Tau criterion for feature selection decision tree and neural network learning

The data collected for various domain purposes usually contains some features irrelevant tothe concept being learned. The presence of these features interferes with the learning mechanism and as a result the predicted models tend to be more complex and less accurate. It is important to employ an effective feature selection strategy so that only the necessary and significant features will be used to learn the concept at hand. The Symmetrical Tau (t) [13] is a statistical-heuristic measure for the capability of an attribute in predicting the class of another attribute, and it has successfully been used as a feature selection criterion during decision tree construction. In this paper we aim to demonstrate some other ways of effectively using the t criterion to filter out the irrelevant features prior to learning (pre-pruning) and after the learning process (post-pruning). For the pre-pruning approach we perform two experiments, one where the irrelevant features are filtered out according to their t value, and one where we calculate the t criterion for Boolean combinations of features and use the highest t-valued combination. In the post-pruning approach we use the t criterion to prune a trained neural network and thereby obtain a more accurate and simple rule set. The experiments are performed on data characterized by continuous and categorical attributes and the effectiveness of the proposed techniques is demonstrated by comparing the derived knowledge models in terms of complexity and accuracy

Human-like rule optimization for continuous domains

When using machine learning techniques for data mining purposes one of the main requirements is that the learned rule set is represented in a comprehensible form. Simpler rules are preferred as they are expected to perform better on unseen data. At the same time the rules should be specific enough so that the misclassification rate is kept to a minimum. In this paper we present a rule optimizing technique motivated by the psychological studies of human concept learning. The technique allows for reasoning to happen at both higher levels of abstraction and lower level of detail in order to optimize the rule set. Information stored at the higher level allows for optimizing processes such as rule splitting, merging and deleting, while the information stored at the lower level allows for determining the attribute relevance for a particular rule. The attributes detected as irrelevant can be removed and the ones previously detected as irrelevant can be reintroduced if necessary. The method is evaluated on the rules extracted from publicly available real world datasets using different classifiers, and the results demonstrate the effectiveness of the presented rule optimizing technique

Towards the mental health ontology

Lots of research have been done within the mental health domain, but exact causes of mental illness are still unknown. Concerningly, the number of people being affected by mental conditions is rapidly increasing and it has been predicted that depression would be the world's leading cause of disabilityby 2020. Most mental health information is found in electronic form. Application of the cutting-edge information technologies within the mental health domain has the potential to greatly increase the value of the available information. Specifically, ontologies form the basis for collaboration between researchteams, for creation of semantic web services and intelligent multi-agent systems, for intelligent information retrieval, and for automatic data analysis such as data mining. In this paper, we present Mental Health Ontology which can be used to underpin a variety of automatic tasks and positively transform the way information is being managed and used within the mental health domain

DYNASTAT: A Methodology for Dynamic and Static Modeling of Multi-agent Systems

Multi-agent systems are increasingly being used within various knowledge domains. The need for modeling of the multi-agent systems in a systematic and effective way is becoming more evident. In this chapter, we present the DYNASTAT methodology. This methodology involves a conceptual overview of multi-agent systems, a selection of specific agent characteristics to model, and a discussion of what has to be modeled for each of these agent characteristics. DYNASTAT is independent of any particular modeling language but provides a framework that can be used to realize a particular language in the context of a real-world example. UML 2.2 was chosen as the modeling language to implement the DYNASTAT methodology and this was illustrated using examples from the medical domain. Several UML 2.2 diagrams were selected including a use case, composite structure, sequence and activity diagram to model a multi-agent system able to assist botha medical researcher and a primary care physician. UML 2.2 provides a framework for effective modeling of agent-based systems in a standardized way which this chapter endeavors to demonstrate