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

Use of anti-terrorism digital ecosystem in the fight against terrorism

In this paper, we propose an Anti-terrorist Digital Ecosystem (ATDES) that enables efficient terrorist identification and protection against terrorist attacks. An Anti-terrorist Digital Environment (ATDE) is designed as being populated by interconnected Anti-terrorist Digital Components (ATDC). ATDC are combined together to support collaboration, cooperation and sharing of available information between various regions, countries and even continents.ATDC may be any useful idea that can be digitalized, transported within the ecosystem and processed by humans or by computers. The key ATDC include ID databases that contain personal records, screening components that read personal records and match them with the available information from the ID databases and machine-readable personal records. The available information is put into one big virtual database and enables matching of personal records.If the available information is to be shared between various ID information resources, standardization of data needs to take place. Ontologies can be used for this purpose. Instantiation of the Ontology concepts result in ID Ontologies that act as personal records. Because Ontology files are machine readable, it is possible to do the matching of personal records with the available ID records from the networked ID databases and to action the results.The significance of this research lies in the unification of the advances of the Ontology technology and Ecosystem paradigm for the purpose of creating a more secure environment in which to fight against terrorism

A framework for detecting financial statement fraud through multiple data sources

This project deals with how to detect fraud and non-compliance in financial statements in the present day in one of the biggest economies in the world, the U.S. Since it is mainly public companies that release detailed financial infor-mation, they are the focus. This project focuses on the top five market sectors where fraud is most common. It focuses on a variety of fraud types, but not on cases of deception that do not constitute fraud. A framework will be proposed which ac-counts for both structured data (the numbers in the balance sheet, income statement and cash flow statement) and unstruc-tured data (the footnotes in these financial statements). It uses ontology-driven data mining techniques to do so

Lipoprotein ontology as a functional knowledge base

The advances of high throughput research in the biomedical domain have resulted in an onslaught of data being generated at an exponential rate. As a result, researchers face challenges in navigating through overwhelming amounts of information in order to derive relevant scientific insights. Ontologies address these issues by providing explicit description of biomedical entities and a platform for the integration of data, thereby enabling a more efficient retrieval of information. There have been major efforts in the development of biomedical ontologies in the recent years; however no such ontology exists for lipoproteins, which play a crucial role in various biological and cellular functions. Dysregulation in lipoprotein metabolism is significantly associated with an increased risk to cardiovascular disease, the leading cause of mortality in the world today. The aim of this paper is to propose a preliminary framework for Lipoprotein Ontology, with particular focus on the etiology and treatment of lipoprotein dysregulation. This may provide a novel and effective strategy for managing at risk individuals

Use of ontology-based multi-agent systems in the biomedical domain

Coordination, cooperation and exchange of information is important to the medical community. We design a new ontology, called Generic Human Disease Ontology (GHDO), by merging and aligning existing medical ontologies. The concepts of the GHDO are organized into the following four dimensions: Types, Symptoms, Causes and Treatments of human diseases. We also design a multi-agent system framework over different information resources. The multi-agent system uses the common GHDO ontology for query formulation, information retrieval and information integration. We conclude that this intelligent dynamic system provides opportunities to collect information from multiple information resources, to share data efficiently and to integrate and manage scientific results in a timely manner

Domain ontology usage analysis framework

The Semantic Web (also known as Web of Data) is growing fast and becoming a decentralized knowledge platform for publishing and sharing information. The web ontologies promote the establishment of a shared understanding between data providers and data consumers, allowing for automated information processing and effective and efficient information retrieval. The majority of existing research efforts is focused around ontology engineering, ontology evaluation and ontology evolution. This work goes a step further and evaluates theontology usage. In this paper, we present an Ontology Usage Analysis Framework (OUSAF) and a set of metrics used to measure the ontology usage. The implementation of the proposed framework is illustrated using the example of GoodRelations ontology (GRO). GRO has been well adopted by the semantic ecommerce community, and the OUSAF approach has been used to analyse GRO usage in the dataset comprised of RDF data collected from the web

Application of Digital Ecosystem Design Methodology Within the Health Domain

We define a digital ecosystem (DES) as the dynamic and synergetic complex of digital communities consisting of interconnected, interrelated, and interdependent digital species situated in a digital environment that interact as a functional unit and are linked together through actions, information, and transaction flows. The design of DESs requires the integration of a number of different and complementary technologies, including agent-based and self-organizing systems, ontologies, swarm intelligence, ambient intelligence, data mining, genetic algorithms, etc. The integration of multiple technologies and the resulting synergetic effects contribute to the creation of highly complex, dynamic, and powerful systems. The application of DESs within different domains has the power to transform these domains by giving them a more intelligent and a more dynamic nature. In this paper, we illustrate how a DES design methodology can be used to systematically create a Digital Health Ecosystem (DHES). We address the key steps associated with the DES design and focus specifically on the use of the electronic health records within the DHES. The design methodology framework illustrated in this paper serves as a navigating tool during the design of DHESs

Creating interoperability within healthcare industry

During the last decade, a number of health initiatives have been undertaken in Australia. However, Australian medical systems still suffer from the chronic problem of inability to share information essential to the health and wellbeing of patients. The major causes for this are (1) the lack of a standardized format in which patient information is being kept, and (2) the lack of infrastructure to enable sharing of the information among different organizations and institutions. In this paper we propose the use of ontologies, to enable effective translation between different EHR formats, and use of web services to enable efficient information exchange and sharing. The proposed solution has the potential to greatly improve the way patient information is being used, and consequently reduce the associated costs in both human and financial terms

Holonic multi-agent system complemented by human disease ontology supporting bio-medical community

The medical milieu is an open environment characterized by a variety of distributed, heterogeneous and autonomous information resources. Coordination, cooperation and exchange of information are important to the medical community. This paper presents an Ontology-based Holonic Multiagent System that combines the advantages of the holonic paradigm with multi-agent system technology and ontology design, in order to realize a highly reliable, adaptive, scalable, flexible and robust diagnostic system for diseases. We design a new ontology, called Generic Human Disease Ontology (GHDO), for the representation of knowledge regarding human diseases. The concepts of the GHDO ontology are organized into the following four dimensions: Types, Symptoms, Causes and Treatments of human diseases. The holonic multi-agent system uses this common GHDO ontology for purpose of query formulation, information retrieval and information integration. This intelligent dynamic system provides opportunities to collect information from multiple information resources, to share data efficiently and to integrate and manage scientific results in a timely manner. We believe such a technique is expected to become the norm once existing resources (e.g. disease databases) will have become unlocked semantically through annotation with a shared ontology

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