Fuzzy logic for decision support in chronic care

Computerized clinical guidelines can provide significant benefits in terms of health outcomes and costs, however, their effective computer implementation presents significant problems. Vagueness and ambiguity inherent in natural language (textual) clinical guidelines makes them problematic for formulating automated alerts or advice. Fuzzy logic allows us to formalize the treatment of vagueness in a decision support architecture. In care plan on-line (CPOL), an intranet-based chronic disease care planning system for general practitioners (GPs) in use in South Australia, we formally treat fuzziness in interpretation of quantitative data, formulation of recommendations and unequal importance of clinical indicators. We use expert judgment on cases, as well as direct estimates by experts, to optimize aggregation operators and treat heterogeneous combinations of conjunction and disjunction that are present in the natural language decision rules formulated by specialist teams.

Appropriate choice of aggregation operators in fuzzy decision support systems

Fuzzy logic provides a mathematical formalism for a unified treatment of vagueness and imprecision that are ever present in decision support and expert systems in many areas. The choice of aggregation operators is crucial to the behavior of the system that is intended to mimic human decision making. This paper discusses how aggregation operators can be selected and adjusted to fit empirical data—a series of test cases. Both parametric and nonparametric regression are considered and compared. A practical application of the proposed methods to electronic implementation of clinical guidelines is presented

Committing to quality learning through adaptive online assessment

With advances in computer-based technologies and the emergence of e-learning, there are unprecedented opportunities to reconsider assessment of learning (and, axiomatically, of teaching) and how this can be undertaken. One approach is adaptive assessment. Although it has existed in the tertiary environment since the time of the oral examination, advanced technologies allow much fuller exploitation of the possibilities inherent in a dynamic system of testing that responds to the user. Having described the characteristics of adaptive assessment, this paper considers how it can achieve significant pedagogical aims within the sector. The paper differentiates between adaptive assessment to assist learning and adaptive assessment to assess achievement. How adaptive assessment can be put in place and salient issues, such as security and system integrity, when such assessment is used for credit, are then discussed. The paper concludes that the capability exists but it has yet to be exploited within higher education as a viable approach to assessment and as a contributor to quality learning.

An agent-based hybrid intelligent system for financial investment planning

Many complex problems including financial investment planning require hybrid intelligent systems that integrate many intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. However, hybrid intelligent systems are difficult to develop due to complicated interactions and technique incompatibilities. This paper describes a hybrid intelligent system for financial investment planning that was built from agent points of view. This system currently consists of 13 different agents. The experimental results show that all agents in the system can work cooperatively to provide reasonable investment advice. The system is very flexible and robust. The success of the system indicates that agent technologies can significantly facilitate the construction of hybrid intelligent systems.

A new blind signal separation algorithm for instantaneous MIMO system

We address the problem of adaptive blind source separation (BSS) from instantaneous multi-input multi-output (MIMO) channels. In this paper, we propose a new constant modulus (CM)-based algorithm which employ nonlinear function as the de-correlation term. Moreover, it is shown by theoretical analysis that the proposed algorithm has less mean square error (MSE), i.e., better separation performance, in steady state than the cross-correlation and constant modulus algorithm (CC-CMA). Numerical simulations show the effectiveness of the proposed result.

A new blind-equalization algorithm for an FIR SIMO system driven by MPSK signal

We consider the problem of blind equalization of a finite impulse response and single-input multiple-output system driven by an M-ary phase-shift-keying signal. The existing single-mode algorithms for this problem include the constant modulus algorithm (CMA) and the multimodulus algorithm (MMA). It has been shown that the MMA outperforms the CMA when the input signal has no more than four constellation points, i.e., Mles4. In this brief, we present a new adaptive equalization algorithm that jointly exploits the amplitude and phase information of the input signal. Theoretical analysis shows that the proposed algorithm has less mean square error, i.e., better equalization performance, at steady state than the CMA regardless of the value of M. The superior performance of our algorithm to the CMA and the MMA is validated by simulation examples

Interpreting Fanger's comfort equation within the adaptive paradigm

This paper reviews the evolution of Fanger's heat balance equation in regard of adaptive opportunities. Heat balance and adaptive response are integrated into one model as two fundamental aspects of human-environment interaction that define thermal comfort perception, rather than being seen as two concepts of alternative comfort paradigms. The paper suggests to extent Fanger's model with a heat storage term in order to account for comfort perception under transient thermal conditions, and to review Fanger's modelling assumptions in order to allow for a greater variety of adaptive response options. In the presented model heat exchange is modulated through adaptation of physiological, environmental and behavioural parameters in the human-environment system defined through Fanger's heat exchange equations. A computational prototype is implemented to determine 'comfortable' values and ranges of the six comfort dimensions alternatively to Fanger's comfort indices. Thereby values of for example 'comfortable' clothing and metabolic rate are results rather than necessary input parameters, which are difficult to determine. This approach allows generating design advice for physical, organisational and social environments based on heat balance calculation in the six-dimensional opportunity space defined through Fanger's comfort equation. A starting point for the development of a dynamic adaptive comfort model is set.

Adaptive communal detection in search of adversarial identity crime

This paper is on adaptive real-time searching of credit application data streams for identity crime with many search parameters. Specifically, we concentrated on handling our domain-specific adversarial activity problem with the adaptive Communal Analysis Suspicion Scoring (CASS) algorithm. CASS's main novel theoretical contribution is in the formulation of State-of- Alert (SoA) which sets the condition of reduced, same, or heightened watchfulness; and Parameter-of-Change (PoC) which improves detection ability with pre-defined parameter values for each SoA. With pre-configured SoA policy and PoC strategy, CASS determines when, what, and how much to adapt its search parameters to ongoing adversarial activity. The above approach is validated with three sets of experiments, where each experiment is conducted on several million real credit applications and measured with three appropriate performance metrics. Significant improvements are achieved over previous work, with the discovery of some practical insights of adaptivity into our domain.

Adaptive spike detection for resilient data stream mining

Automated adversarial detection systems can fail when under attack by adversaries. As part of a resilient data stream mining system to reduce the possibility of such failure, adaptive spike detection is attribute ranking and selection without class-labels. The first part of adaptive spike detection requires weighing all attributes for spiky-ness to rank them. The second part involves filtering some attributes with extreme weights to choose the best ones for computing each example’s suspicion score. Within an identity crime detection domain, adaptive spike detection is validated on a few million real credit applications with adversarial activity. The results are F-measure curves on eleven experiments and relative weights discussion on the best experiment. The results reinforce adaptive spike detection’s effectiveness for class-label-free attribute ranking and selection.

Adaptive load shedding for mining frequent patterns from data streams

Most algorithms that focus on discovering frequent patterns from data streams assumed that the machinery is capable of managing all the incoming transactions without any delay; or without the need to drop transactions. However, this assumption is often impractical due to the inherent characteristics of data stream environments. Especially under high load conditions, there is often a shortage of system resources to process the incoming transactions. This causes unwanted latencies that in turn, affects the applicability of the data mining models produced – which often has a small window of opportunity. We propose a load shedding algorithm to address this issue. The algorithm adaptively detects overload situations and drops transactions from data streams using a probabilistic model. We tested our algorithm on both synthetic and real-life datasets to verify the feasibility of our algorithm.

The design and implementation of a traffic accident analysis system

We designed and implemented a traffic accident analysis system (TAAS) in the paper. TAAS is the system faced traffic accident analysis, which uses the traffic rules (law) as knowledge sources to judge if the driver is responsible for a traffic accident. TAAS has characteristics of separating knowledge base and inference engine, using production rule and backward chaining. Besides, TAAS used predefined text and tracing program to realize explanation mechanism.

Fuzzy logic in clinical practice decision support systems

Computerized clinical guidelines can provide significant benefits to health outcomes and costs, however, their effective implementation presents significant problems. Vagueness and ambiguity inherent in natural (textual) clinical guidelines is not readily amenable to formulating automated alerts or advice. Fuzzy logic allows us to formalize the treatment of vagueness in a decision support architecture. This paper discusses sources of fuzziness in clinical practice guidelines. We consider how fuzzy logic can be applied and give a set of heuristics for the clinical guideline knowledge engineer for addressing uncertainty in practice guidelines. We describe the specific applicability of fuzzy logic to the decision support behavior of Care Plan On-Line, an intranet-based chronic care planning system for General Practitioners.

A generalized data windowing scheme for adaptive conjugate gradient algorithms

The performance of the modified adaptive conjugate gradient (CG) algorithms based on the iterative CG method for adaptive filtering is highly related to the ways of estimating the correlation matrix and the cross-correlation vector. The existing approaches of implementing the CG algorithms using the data windows of exponential form or sliding form result in either loss of convergence or increase in misadjustment. This paper presents and analyzes a new approach to the implementation of the CG algorithms for adaptive filtering by using a generalized data windowing scheme. For the new modified CG algorithms, we show that the convergence speed is accelerated, the misadjustment and tracking capability comparable to those of the recursive least squares (RLS) algorithm are achieved. Computer simulations demonstrated in the framework of linear system modeling problem show the improvements of the new modifications.

Stability and convergence analysis of transform-domain LMS adaptive filters with second-order autoregressive process

In this paper, the stability and convergence properties of the class of transform-domain least mean square (LMS) adaptive filters with second-order autoregressive (AR) process are investigated. It is well known that this class of adaptive filters improve convergence property of the standard LMS adaptive filters by applying the fixed data-independent orthogonal transforms and power normalization. However, the convergence performance of this class of adaptive filters can be quite different for various input processes, and it has not been fully explored. In this paper, we first discuss the mean-square stability and steady-state performance of this class of adaptive filters. We then analyze the effects of the transforms and power normalization performed in the various adaptive filters for both first-order and second-order AR processes. We derive the input asymptotic eigenvalue distributions and make comparisons on their convergence performance. Finally, computer simulations on AR process as well as moving-average (MA) process and autoregressive-moving-average (ARMA) process are demonstrated for the support of the analytical results.

Decision making to support an integrated infrastructure supply chain industrial ecological approach within public private partnerships

Supply chains are complex adaptive systems for which final performance depends upon numerous interdependent decisions made by numerous firms which synthesise inputs from various resources systems.  The dynamic interdependent behaviour of social, economic, material and informational resource systems within eco-industrial settings that support the built environment life cycle supply chains can be studied at the supply chain level.  The impact of megaprojects is significant and holds promise to explore the impact of decisions on various systems as it combines project and system boundaries.  Megaoprojects considered as major events within systems can produce critical revolutionary impacts on the systems within which they are embedded.  The decisions that are made on megaprojects are central to risk management.  typically major infrastructure projects are procured through a form of public private partnership (PPP).  The core principle of PPP is value for money which refers to the best available outcome attempting to take account of all benefits, costs and risks over the whole life of the procurement.  In this paper the focus is on Australia where there has been considerable acitivity in the use of PPPs.  With recent national infrastucture packages proposed to stimulate the economy due to the global financial crisis, decision modelling on risks is a revelant and critical matter not only in practice but also in the research community.  PPPs encourage the whole-of-lifecycle approach in the procurement and management of public sector assets by transparently recognising the costs and risks associated with the whole life of the required service or facility, thus integrated whole of life supply chains can be considered.  By creating a single point of responsibility for an entire project from inception through operation, a strong incentive is created for thinking about the effects that a design or construction decision will have on the effectiveness and efficiency of managing and maintaining a facility during its operational life.  The decision to procure holistic supply chains becomes a much more viable commercial reality in the PPP environment than previously considered in the usual commercial construction spot transactional approach.  These types of decisions tend to be imprecise, approximate and complex requireing justification and reasoning logic rather than the classical 'truth' logic.  The purpose of this paper is to develop a theoretical decision framework which combines interdependency and multi-values logic for supply chain procurement modelling.