Computerised Diagnostic Decision Support System for the Classification of Pre-Invasive Cervical Squamous Lesions.

Previous studies have revealed considerable interobserver and intraobserver variation in the histological classification of preinvasive cervical squamous lesions. The aim of the present study was to develop a decision support system (DSS) for the histological interpretation of these lesions. Knowledge and uncertainty were represented in the form of a Bayesian belief network that permitted the storage of diagnostic knowledge and, for a given case, the collection of evidence in a cumulative manner that provided a final probability for the possible diagnostic outcomes. The network comprised 8 diagnostic histological features (evidence nodes) that were each independently linked to the diagnosis (decision node) by a conditional probability matrix. Diagnostic outcomes comprised normal; koilocytosis; and cervical intraepithelial neoplasia (CIN) 1, CIN II, and CIN M. For each evidence feature, a set of images was recorded that represented the full spectrum of change for that feature. The system was designed to be interactive in that the histopathologist was prompted to enter evidence into the network via a specifically designed graphical user interface (i-Path Diagnostics, Belfast, Northern Ireland). Membership functions were used to derive the relative likelihoods for the alternative feature outcomes, the likelihood vector was entered into the network, and the updated diagnostic belief was computed for the diagnostic outcomes and displayed. A cumulative probability graph was generated throughout the diagnostic process and presented on screen. The network was tested on 50 cervical colposcopic biopsy specimens, comprising 10 cases each of normal, koilocytosis, CIN 1, CIN H, and CIN III. These had been preselected by a consultant gynecological pathologist. Using conventional morphological assessment, the cases were classified on 2 separate occasions by 2 consultant and 2 junior pathologists. The cases were also then classified using the DSS on 2 occasions by the 4 pathologists and by 2 medical students with no experience in cervical histology. Interobserver and intraobserver agreement using morphology and using the DSS was calculated with K statistics. Intraobserver reproducibility using conventional unaided diagnosis was reasonably good (kappa range, 0.688 to 0.861), but interobserver agreement was poor (kappa range, 0.347 to 0.747). Using the DSS improved overall reproducibility between individuals. Using the DSS, however, did not enhance the diagnostic performance of junior pathologists when comparing their DSS-based diagnosis against an experienced consultant. However, the generation of a cumulative probability graph also allowed a comparison of individual performance, how individual features were assessed in the same case, and how this contributed to diagnostic disagreement between individuals. Diagnostic features such as nuclear pleomorphism were shown to be particularly problematic and poorly reproducible. DSSs such as this therefore not only have a role to play in enhancing decision making but also in the study of diagnostic protocol, education, self-assessment, and quality control. (C) 2003 Elsevier Inc. All rights reserved.

Extending BPM Environments of Your Choice with Performance Related Decision Support

What-if Simulations have been identified as one solution for business performance related decision support. Such support is especially useful in cases where it can be automatically generated out of Business Process Management (BPM) Environments from the existing business process models and performance parameters monitored from the executed business process instances. Currently, some of the available BPM Environments offer basic-level performance prediction capabilities. However, these functionalities are normally too limited to be generally useful for performance related decision support at business process level. In this paper, an approach is presented which allows the non-intrusive integration of sophisticated tooling for what-if simulations, analytic performance prediction tools process optimizations or a combination Of Such solutions into already existing BPM environments. The approach abstracts from process modelling techniques which enable automatic decision support spanning processes across numerous BPM Environments. For instance, this enables end-to-end decision support for composite processes modelled with the Business Process Modelling Notation (BPMN) on top of existing Enterprise Resource Planning (ERP) processes modelled with proprietary languages.

Climate change adaptation : a decision support framework to encourage environmentally responsible behaviour

Structured Abstract:
Purpose: Very few studies investigate environmentally responsible behaviour (ERB). This paper presents a new 'Awareness Behaviour Intervention Action' (ABIA) Decision Support Framework to sustain ERB.

Design/methodology/approach: Previous ERB programmes have failed to deliver lasting results; they have not appropriately understood and provided systems to address ERB (Costanzo et al., 1986). These programmes were based on assumptions (Moloney et al., 2010), which this paper addresses. The ABIA Framework has been developed through a case study of social housing tenants waiting for low or zero carbon homes.

Findings: The ABIA Framework enables a better understanding of current attitudes to environmental issues and provides support for ERB alongside technological interventions employed to promote and sustain carbon reduction.

Research limitations/implications: The ABIA Framework should be tested on individuals and communities in a variety of socio-economic, political and cultural contexts. This will help unpack how it can impact on the behaviours of individuals and communities including stakeholders.

Practical implications: This type of research and the ABIA Framework developed from it are crucial if the UK pledge to become the first country in the World where all new homes from 2016 are to be zero carbon.

Social implications: The Framework encourages both individual and community discussion and solving of sustainability issues.

Originality/value: There are few, if any, studies that have developed a framework which can be used to support behavioural change for adaptation to sustainable living in low or zero carbon homes.

Farmer perception of suitable conditions for slurry application compared with decision support system recommendations

The application of slurry nutrients to land can be associated with unintended losses to the environment depending on soil and weather conditions. Correct timing of slurry application, however, can increase plant nutrient uptake and reduce losses. A decision support system (DSS), which predicts optimum conditions for slurry spreading based on the Hybrid Soil Moisture Deficit (HSMD) model, was investigated for use as a policy tool. The DSS recommendations were compared to farmer perception of suitable conditions for slurry spreading for three soil drainage classes (well, moderate and poorly drained) to better understand on farm slurry management practices and to identify potential conflict with farmer opinion. Six farmers participated in a survey over two and a half years, during which they completed a daily diary, and their responses were compared to Soil Moisture Deficit (SMD) calculations and weather data recorded by on farm meteorological stations. The perception of land drainage quality differed between farmers and was related to their local knowledge and experience. It was found that the allocation of grass fields to HSMD drainage classes using a visual assessment method aligned farmer perception of drainage at the national scale. Farmer opinion corresponded to the theoretical understanding that slurry should not be applied when the soil is wetter than field capacity, i.e. when drainage can occur. While weather and soil conditions (especially trafficability) were the principal reasons given by farmers not to spread slurry, farm management practices (grazing and silage) and current Nitrates Directive policies (closed winter period for spreading) combined with limited storage capacities were obstacles to utilisation of slurry nutrients. Despite the slightly more restrictive advice of the DSS regarding the number of suitable spreading opportunities, the system has potential to address an information deficit that would help farmers to reduce nutrient losses and optimise plant nutrient uptake by improved slurry management. The DSS advice was in general agreement with the farmers and, therefore, they should not be resistant to adopting the tool for day to day management.

An Extended Event Reasoning Framework for Decision Support under Uncertainty

To provide in-time reactions to a large volume of surveil- lance data, uncertainty-enabled event reasoning frameworks for CCTV and sensor based intelligent surveillance system have been integrated to model and infer events of interest. However, most of the existing works do not consider decision making under uncertainty which is important for surveillance operators. In this paper, we extend an event reasoning framework for decision support, which enables our framework to predict, rank and alarm threats from multiple heterogeneous sources.