Jinan Fiaidhi

Real-Time Clinical Decision Support System with Data Stream Mining

This research aims to describe a new design of data stream mining system that can analyze medical data stream and make real-time prediction. The motivation of the research is due to a growing concern of combining software technology and medical functions for the development of software application that can be used in medical field of chronic disease prognosis and diagnosis, children healthcare, diabetes diagnosis, and so forth. Most of the existing software technologies are case-based data mining systems. They only can analyze finite and structured data set and can only work well in their early years and can hardly meet todays medical requirement. In this paper, we describe a clinical-support-system based data stream mining technology; the design has taken into account all the shortcomings of the existing clinical support systems.

Improving the classification performance of biological imbalanced datasets by swarm optimization algorithms

Classification which is a popular supervised machine learning method has many applications in computational biology, where data samples are automatically categorized into predefined labels with the aid of data mining. Often the training samples contain very few instances of interest (e.g., medical anomalies, rare disease in a population, and unusual syndromes, etc.), but many normal instances. Such imbalanced ratio of data distributions among the target labels hampers the efficacy of classification algorithms, because the induced model has not been trained with sufficient amount of instances of the interesting label(s), but overwhelmed with ordinary training records. Traditional remedies attempt to rebalance the data distributions of the target classes, by inflating the interesting instances artificially, reducing the majority of the common instances or a combination of both. Though the fundamental concept is effective, there is no clear guideline on how to strike a balance between fabricating the rare samples and reducing the norms, with the purpose of maximizing the classification accuracy. In this paper, an optimization model using different swarm strategies (Bat-inspired algorithm and PSO) is proposed for adaptively balancing the increase/decrease of the class distribution, depending on the properties of the biological datasets. The optimization is extended for achieving the highest possible accuracy and Kappa statistics at the same time as well. The optimization model is tested on five imbalanced medical datasets, which are sourced from lung surgery logs and virtual screening of bioassay data. Computer simulation results show that the proposed optimization model outperforms other class balancing methods in medical data classification.

Enforcing Multitenancy for Cloud Computing Environments

Learn about the articles in this special issue and how to enforce multitenancy in cloud computing environments.

Hot Topics in Cloud Computing

Cloud computing is no longer just hype. Its quickly evolving and gradually realizing its business value, as the articles in this special issue show. Its now attracting more and more researchers and practitioners, who are creating innovations around its core enabling technologies and architectural building blocks.

HCX: A Distributed OSGi Based Web Interaction System for Sharing Health Records in the Cloud

WITH the maturity of Web Services and Enterprise Service Oriented Architectures (SOA), new delivery and Web interaction models are now demonstrating how services can be traded outside traditional ownership and provisioning boundaries. The value of SOA comes from having an architecture that readily accommodates change. The more your business changes, the more SOA pays for itself. However, the initial build-out of SOA, prior to business change or service sharing, is cost-ineffective. By incorporating cloud computing in SOA, the time to value is shortened because you leverage ‘other people’s work’ as well as saving on infrastructure cost by leveraging on demand cloud based infrastructure services. This article outlines a distributed Web interactive system for sharing health records on the cloud using distributed OSGi services and consumers, called HCX (Health Cloud eXchange). This system allows for different health record and related healthcare services to be dynamically discovered and interactively used by client programs running within a federated private cloud. A basic prototype is presented as proof of concept along with a description to the steps and processes involved in setting up the underlying infrastructure. Finally some future directions for using this infrastructure are illustrated.

The Next Step for Learning Analytics

Learning Analytics Initiatives Learning analytics is the third wave of developments in instructional technology, which began with the advent of the learning management system (LMS) in 1991. The second wave integrated the LMS into the wider educational enterprise by involving learners on social networks (also known as the Web 2.0 wave). During this third wave, learning analytics as a term has been significantly popularized by the Educause International Conferences on Learning Analytics and Knowledge (LAK),2 which started in 2011 (https://tekri.athabascau. ca/analytics). Learning analytics focuses on collecting and analyzing data from a variety of sources to provide information on what works (and what doesn’t) with respect to teaching and learning.3,4 This helps educational institutions improve their quality of learning and overall competitiveness. Consequently, many research communities have developed a variety of promising initiatives, models, and applications to improve learner success. For example, Santa Monica College’s Glass Classroom initiative,5 introduced in December 2012, aims to enhance student and teacher performance by collecting and analyzing large amounts of data. Using realtime feedback of the student’s performance, Glass Class room adjusts the courseware to meet educational objectives. Another example is at the University of Wisconsin-Madison. Since May 2012, the university has been working to develop a data-driven “early-warning” system that faculty and advisors can use to support student academic success.6 The system will help identify academically at-risk students, using nontraditional indicators that can be gathered early in a student’s career, even at the beginning of a semester. The system aims to intervene early, improve students’ academic success, and bolster the campus’s retention and graduation rates. Furthermore, many research groups and societies are providing excellent networks for researchers who are exploring the impact of analytics on teaching, learning, training, and development (see the “Related Learning Analytics Research” sidebar). In particular, these groups and societies are promoting several learning analytics models, which have been Related Learning Analytics Research

Stream-based Biomedical Classification Algorithms for Analyzing Biosignals

Classification in biomedical applications is an important task that predicts or classifies an outcome based on a given set of input variables such as diagnostic tests or the symptoms of a patient. Traditionally the classification algorithms would have to digest a stationary set of historical data in order to train up a decision-tree model and the learned model could then be used for testing new samples. However, a new breed of classification called stream-based classification can handle continuous data streams, which are ever evolving, unbound, and unstructured, for instance--biosignal live feeds. These emerging algorithms can potentially be used for real-time classification over biosignal data streams like EEG and ECG, etc. This paper presents a pioneer effort that studies the feasibility of classification algorithms for analyzing biosignals in the forms of infinite data streams. First, a performance comparison is made between traditional and stream-based classification. The results show that accuracy declines intermittently for traditional classification due to the requirement of model re-learning as new data arrives. Second, we show by a simulation that biosignal data streams can be processed with a satisfactory level of performance in terms of accuracy, memory requirement, and speed, by using a collection of stream-mining algorithms called Optimized Very Fast Decision Trees. The algorithms can effectively serve as a corner-stone technology for real-time classification in future biomedical applications.

Developing a secure distributed OSGI cloud computing infrastructure for sharing health records

Cloud Computing has become an emerging computing paradigm which brings new opportunities and challenges to overcome. While the cloud provides seemingly limitless scalability and an alternative to expensive data center infrastructure, it raises new issues in regards to security and privacy as processing and storage tasks are handed over to third parties. This article outlines a Distributed OSGi (DOSGi) architecture for sharing electronic health records utilizing public and private clouds which overcomes some of the security issues inherent in cloud systems. This system, called HCX (Health Cloud eXchange), allows for health records and related healthcare services to be dynamically discovered and interactively used by client programs accessing services within a federated cloud. A basic prototype is presented as proof of concept along with a description of the steps and processes involved in setting up the underlying security services. Several improvements have been added to HCX including a Role-Based Single-Sign-On (RBSSO).

Evaluation of Stream Mining Classifiers for Real-Time Clinical Decision Support System: A Case Study of Blood Glucose Prediction in Diabetes Therapy

Earlier on, a conceptual design on the real-time clinical decision support system (rt-CDSS) with data stream mining was proposed and published. The new system is introduced that can analyze medical data streams and can make real-time prediction. This system is based on a stream mining algorithm called VFDT. The VFDT is extended with the capability of using pointers to allow the decision tree to remember the mapping relationship between leaf nodes and the history records. In this paper, which is a sequel to the rt-CDSS design, several popular machine learning algorithms are investigated for their suitability to be a candidate in the implementation of classifier at the rt-CDSS. A classifier essentially needs to accurately map the events inputted to the system into one of the several predefined classes of assessments, such that the rt-CDSS can follow up with the prescribed remedies being recommended to the clinicians. For a real-time system like rt-CDSS, the major technological challenges lie in the capability of the classifier to process, analyze and classify the dynamic input data, quickly and upmost reliably. An experimental comparison is conducted. This paper contributes to the insight of choosing and embedding a stream mining classifier into rt-CDSS with a case study of diabetes therapy.

An adaptive meta-heuristic search for the internet of things

Abstract The number of sensors deployed around the world is growing at a rapid pace when we are moving towards the Internet of Things (IoT). The widespread deployment of these sensors represents significant financial investment and technical achievement. These sensors continuously generate enormous amounts of data which is capable of supporting an almost unlimited set of high value proposition applications for users. Given that, effectively and efficiently searching and selecting the most related sensors of a user’s interest has recently become a crucial challenge. In this paper, inspired by ant clustering algorithm, we propose an effective context-aware method to cluster sensors in the form of Sensor Semantic Overlay Networks (SSONs) in which sensors with similar context information are gathered into one cluster. Firstly, sensors are grouped based on their types to create SSONs. Then, our meta-heuristic algorithm called AntClust has been performed to cluster sensors using their context information. Furthermore, useful adjustments have been applied to reduce the cost of sensor search process and an adaptive strategy is proposed to maintain the performance against dynamicity in the IoT environment. Experiments show the scalability and adaptability of AntClust in clustering sensors. It is significantly faster on sensor search when compared with other approaches.