Thomas M. Gatton

Wireless Health Data Exchange for Home Healthcare Monitoring Systems

Ubiquitous home healthcare systems have been playing an increasingly significant role in the treatment and management of chronic diseases, such as diabetes and hypertension, but progress has been hampered by the lack of standardization in the exchange of medical health care information. In an effort to establish standardization, this paper proposes a home healthcare monitoring system data exchange scheme between the HL7 standard and the IEEE1451 standard. IEEE1451 is a standard for special sensor networks, such as industrial control and smart homes, and defines a suite of interfaces that communicate among heterogeneous networks. HL7 is the standard for medical information exchange among medical organizations and medical personnel. While it provides a flexible data exchange in health care domains, it does not provide for data exchange with sensors. Thus, it is necessary to develop a data exchange schema to convert data between the HL7 and the IEEE1451 standard. This paper proposes a schema that can exchange data between HL7 devices and the monitoring device, and conforms to the IEEE 1451 standard. The experimental results and conclusions of this approach are presented and show the feasibility of the proposed exchange schema.

A monitoring and advisory system for diabetes patient management using a rule-based method and KNN.

Diabetes is difficult to control and it is important to manage the diabetic’s blood sugar level and prevent the associated complications by appropriate diabetic treatment. This paper proposes a system that can provide appropriate management for diabetes patients, according to their blood sugar level. The system is designed to send the information about the blood sugar levels, blood pressure, food consumption, exercise, etc., of diabetes patients, and manage the treatment by recommending and monitoring food consumption, physical activity, insulin dosage, etc., so that the patient can better manage their condition. The system is based on rules and the K Nearest Neighbor (KNN) classifier algorithm, to obtain the optimum treatment recommendation. Also, a monitoring system for diabetes patients is implemented using Web Services and Personal Digital Assistant (PDA) programming.

Keyword Extraction from Documents Using a Neural Network Model

A document surrogate is usually represented in a list of words. Because not all words in a document reflect its content, it is necessary to select important words from the document that relate to its content. Such important words are called keywords and are selected with a particular equation based on Term Frequency (TF) and Inverted Document Frequency (IDF). Additionally, the position of each word in the document and the inclusion of the word in the title should be considered to select keywords among words contained in the text. The equation based on these factors gets too complicated to be applied to the selection of keywords. This paper proposes a neural network back propagation model in which these factors are used as the features and feature vectors are generated to select keywords. This paper will show that the proposed neural network backpropagation approach outperforms the equation in distinguishing keywords.

A multi-agent based user context Bayesian neural network analysis system

The increasing user mobility demands placed upon IT services necessitates an environment that enables users to access optimal services at any time and in any place. This study presents research conducted to develop a system that is capable of analyzing user IT service patterns and tendencies and provides the necessary service resources by sharing each user’s context information. First, each user’s context information is gathered to provide the multi-agent software training data necessary to describe user operations in a hybrid peer-to-peer (P2P) structured communication environment. Next, the data collected about each user’s mobile device is analyzed through a Bayesian based neural network system to identify the user’s tendency and extract essential service information. This information provides a communication configuration allowing the user access to the best communication service between the user’s mobile device and the local server at any time and in any place, thereby enhancing the ubiquitous computing environment.

An Intelligent Type 2 Diabetic Patient Management System Using Evolutionary Computation and FCM Algorithm

The present study proposes a management system combining fuzzy c-means (FCM) and evolutionary computation in order to provide an optimal treatment method based on the patients context information in ubiquitous environment. Because FCM has the shortcoming of falling easily into a local solution, we adjusted the initial values sensitively through evolutionary computation. In fitness evaluation, we used Bayesian validation so that superior solutions are selected, and in performance evaluation, experiment and evaluation were made with type 2 diabetic patients.

A function-based user authority delegation model

User authority delegation is granting or withdrawing access to computer-based information by entities that own and/or control that information. These entities must consider who should be granted access to specific information in the organization and determine reasonable authority delegation. Role Based Access Control (RBAC) delegation management, where user access authority is granted for the minimum resources necessary for users to perform their tasks, is not suitable for the actual working environment of an organization. Currently, RBAC implementations cannot correctly model inheritance and rules for different delegations are in conflict. Further, these systems require that user roles, positions, and information access be continuously and accurately updated, resulting in a manual, error-prone access delegation system. This paper presents a proposal for a new authority delegation model, which allows users to identify their own function-based delegation requirements as the initial input to the RBAC process. The conditions for delegations are identified and functions to implement these delegations are defined. The criteria for basic authority delegation, authentication and constraints are quantified and formulated for evaluation. An analysis of the proposed model is presented showing that this approach both minimizes errors in delegating authority and is more suitable for authority delegation administration in real organizational applications.

An evolution based biosensor receptor DNA sequence generation algorithm.

A biosensor is composed of a bioreceptor, an associated recognition molecule, and a signal transducer that can selectively detect target substances for analysis. DNA based biosensors utilize receptor molecules that allow hybridization with the target analyte. However, most DNA biosensor research uses oligonucleotides as the target analytes and does not address the potential problems of real samples. The identification of recognition molecules suitable for real target analyte samples is an important step towards further development of DNA biosensors. This study examines the characteristics of DNA used as bioreceptors and proposes a hybrid evolution-based DNA sequence generating algorithm, based on DNA computing, to identify suitable DNA bioreceptor recognition molecules for stable hybridization with real target substances. The Traveling Salesman Problem (TSP) approach is applied in the proposed algorithm to evaluate the safety and fitness of the generated DNA sequences. This approach improves efficiency and stability for enhanced and variable-length DNA sequence generation and allows extension to generation of variable-length DNA sequences with diverse receptor recognition requirements.

Diagnostic Knowledge Acquisition for Agent-Based Medical Applications

The development of ubiquitous systems for maintenance and control of treatment systems to assist individuals in managing their medical treatment plan would provide an improved system for home healthcare. The assistance of agent-based systems to help doctors and further automate medical treatment systems is limited by medical knowledge data mining accuracy and the complexity of each patients health history and individual. Automation of knowledge base development for each individual patient would allow efficient personalization of each patients treatment plan and allow integration of the doctors individual diagnosis and treatment plan. This paper presents an overview of agent based technologies and describes both historical and state-of-the-art applications of agent technologies in the medical field. Current research and development activity is identified and an algorithm to address the knowledge acquisition bottleneck for diagnostic medical knowledge is present. The algorithm can reduce time consuming knowledge acquisition and allow efficient development of individually tailored medical treatment knowledge bases.

Optimization of fuzzy rules: integrated approach for classification problems

This paper proposes a GA and GDM-based method for removing the unnecessary rules and generating the relevant rules from the fuzzy rules corresponding to several fuzzy partitions. The aim of the proposed method is to find a minimum set of fuzzy rules that can correctly classify all the training patterns. This is achieved by formulating and solving a combinatorial optimization problem that has two objectives: to maximize the number of correctly classified patterns and to minimize the number of fuzzy rules. The fuzzy inference is structured by a set of simple fuzzy rules. In each rule, the antecedent part is made up of the membership functions of a fuzzy set, and the consequent part is made up of a real number. The membership functions and the number of fuzzy inference rules are tuned by means of the GA, while the real numbers in the consequent parts of the rules are tuned by means of the gradient descent method. In order to prove the effectiveness of the proposed method, computer simulation results are shown.

Web-document filtering using concept graph

This paper introduces a retrieval method based on conceptual graph. A hyperlink information is essential to construct conceptual graph. The information is very useful as it provides summary and further linkage to construct conceptual graph that has been provided by human. It also has a property which shows review, relation, hierarchy, generality, and visibility. Using this property, we extracted the keywords of web documents and made up of the conceptual graph among the keywords sampled from web pages. This paper extracts the keywords of web pages using anchor text one out of hyperlink information and makes hyperlink of web pages abstract as the link relation between keywords of each web page. I suggest this useful retrieval method providing querying word extension or domain knowledge by conceptual graph of keywords.