Inclusion Of Expert Knowledge Research Articles

The Correlated Beta Dose Optimisation Approach: Optimal Vaccine Dosing Using Mathematical Modelling and Adaptive Trial Design.

Mathematical modelling methods and adaptive trial design are likely to be effective for optimising vaccine dose but are not yet commonly used. This may be due to uncertainty with regard to the correct choice of parametric model for dose-efficacy or dose-toxicity. Non-parametric models have previously been suggested to be potentially useful in this situation. We propose a novel approach for locating optimal vaccine dose based on the non-parametric Continuous Correlated Beta Process model and adaptive trial design. We call this the 'Correlated Beta' or 'CoBe' dose optimisation approach. We evaluated the CoBe dose optimisation approach compared to other vaccine dose optimisation approaches using a simulation study. Despite using simpler assumptions than other modelling-based methods, we found that the CoBe dose optimisation approach was able to effectively locate the maximum efficacy dose for both single and prime/boost administration vaccines. The CoBe dose optimisation approach was also effective in finding a dose that maximises vaccine efficacy and minimises vaccine-related toxicity. Further, we found that these modelling methods can benefit from the inclusion of expert knowledge, which has been difficult for previous parametric modelling methods. This work further shows that using mathematical modelling and adaptive trial design is likely to be beneficial to locating optimal vaccine dose, ensuring maximum vaccine benefit and disease burden reduction, ultimately saving lives.

Semantic Labeling of Globally Distributed Urban and Nonurban Satellite Images Using High-Resolution SAR Data

While the analysis and understanding of multispectral (i.e., optical) remote sensing images have made considerable progress during the last decades, the automated analysis of synthetic aperture radar (SAR) satellite images still needs some innovative techniques to support nonexpert users in the handling and interpretation of these big and complex data. In this article, we present a survey of existing multispectral and SAR land cover image datasets. To this end, we demonstrate how an advanced SAR image analysis system can be designed, implemented, and verified that is capable of generating semantically annotated classification results (e.g., maps) as well as local and regional statistical analytics such as graphical charts. The initial classification is made based on Gabor features and followed by class assignments (labeling). This is followed by inclusion. This can be accomplished by the inclusion of expert knowledge via active learning with selected examples, and the extraction of additional knowledge from public databases to refine the classification results. Then, based on the generated semantics, we can create new topic models, find typical country-specific phenomena and distributions, visualize them interactively, and present significant examples including confusion matrices. This semiautomated and flexible methodology allows several annotation strategies, the inclusion of dedicated analytics procedures, and can generate broad as well as detailed semantic (multi-)labels for all continents, and statistics or models for selected countries and cities. Here, we employ knowledge graphs and exploit ontologies. These components could already be validated successfully. The proposed methodology can also be adapted to other SAR instruments with different resolutions as well as to multispectral images.

Integrating expert knowledge and ecological niche models to estimate Mexican primates’ distribution

Ecological niche modeling is used to estimate species distributions based on occurrence records and environmental variables, but it seldom includes explicit biotic or historical factors that are important in determining the distribution of species. Expert knowledge can provide additional valuable information regarding ecological or historical attributes of species, but the influence of integrating this information in the modeling process has been poorly explored. Here, we integrated expert knowledge in different stages of the niche modeling process to improve the representation of the actual geographic distributions of Mexican primates (Ateles geoffroyi, Alouatta pigra, and A. palliata mexicana). We designed an elicitation process to acquire information from experts and such information was integrated by an iterative process that consisted of reviews of input data by experts, production of ecological niche models (ENMs), and evaluation of model outputs to provide feedback. We built ENMs using the maximum entropy algorithm along with a dataset of occurrence records gathered from a public source and records provided by the experts. Models without expert knowledge were also built for comparison, and both models, with and without expert knowledge, were evaluated using four validation metrics that provide a measure of accuracy for presence-absence predictions (specificity, sensitivity, kappa, true skill statistic). Integrating expert knowledge to build ENMs produced better results for potential distributions than models without expert knowledge, but a much greater improvement in the transition from potential to realized geographic distributions by reducing overprediction, resulting in better representations of the actual geographic distributions of species. Furthermore, with the combination of niche models and expert knowledge we were able to identify an area of sympatry between A. palliata mexicana and A. pigra. We argue that the inclusion of expert knowledge at different stages in the construction of niche models in an explicit and systematic fashion is a recommended practice as it produces overall positive results for representing realized species distributions.

Content Based Image Retrieval Approach in Creating an Effective Feature Index for Lung Nodule Detection with the Inclusion of Expert Knowledge and Proven Pathology

The paper investigates four major issues in the active field of lung computer aided diagnosis (CAD) using content-based image retrieval (CBIR), which are: creating an efficient feature index for lung nodules for similarity measures, database creation of nodules with proven pathology, robust CBIR system and present a self-diagnosing environment to assist the physician in taking the right decision at right time. The results definitely improves the radiologists performance of detecting suspicious nodules based on the ground truth prepared. CBIR has been implemented to expand the small ground truth of 17 nodules to ground truth of 114 nodules based on available biopsy report. Nine out of 83 different extracted features have been considered as the best discriminating features to classify the lung nodules in three classes: Malignant, Benign and Metastasis. LIDC database has been analysed and achieved an average precision of 92.8% , mean average precision (MAP) of 82% at recall 0.1 and an average precision of 88% with PGIMER, Chandigarh. Results in this paper also indicate that the unnecessary biopsies can be avoided as the results are having few number of false positives which can directly increase the specificity of the proposed research. Keywords: CBIR, CAD, classification, lung cancer, nodules, LIDC.

A GUIDELINE FOR DEVELOPING AN INITIAL HYDROLOGICAL MONITORING NETWORK AS A BASIS FOR WATER MANAGEMENT IN ARTIFICIALLY DRAINED WETLANDS

ABSTRACTReliable hydrological monitoring is the basis for sound water management in drained wetlands. Since statistical methods cannot be employed for unobserved or sparsely monitored areas, the primary design (first set‐up) may be arbitrary in most instances. The objective of this paper is therefore to provide a guideline for designing the initial hydrological monitoring network. A scheme is developed that handles different parts of monitoring and hydrometry in wetlands, focusing on the positioning of surface water and groundwater gauges. For placement of the former, control units are used which correspond to areas whose water levels can be regulated separately. The latter are arranged depending on hydrological response units, defined by combinations of soil type and land use, and the chosen surface water monitoring sites. A practical application of the approach is shown for an investigation area in the Spreewald region in north‐east Germany. The presented scheme leaves a certain degree of freedom to its user, allowing the inclusion of expert knowledge or special concerns. Based on easily obtainable data, the developed hydrological network serves as a first step in the iterative procedure of monitoring network optimisation. Copyright © 2013 John Wiley & Sons, Ltd.

Expert-driven label fusion in multi-atlas-based segmentation of the prostate using weighted atlases

Automated segmentation is required for radiotherapy treatment planning, and multi-atlas methods are frequently used for this purpose. The combination of multiple intermediate results from multi-atlas segmentation into a single segmentation map can be achieved by label fusion. A method that includes expert knowledge in the label fusion phase of multi-atlas-based segmentation was developed. The method was tested by application to prostate segmentation, and the accuracy was compared to standard techniques. The selective and iterative method for performance level estimation (SIMPLE) algorithm for label fusion was modified with a weight map given by an expert that indicates the importance of each region in the evaluation of segmentation results. Voxel-based weights specified by an expert when performing the label fusion step in atlas-based segmentation were introduced into the modified SIMPLE algorithm. These weights incorporate expert knowledge on accuracy requirements in different regions of a segmentation. Using this knowledge, segmentation accuracy in regions known to be important can be improved by sacrificing segmentation accuracy in less important regions. Contextual information such as the presence of vulnerable tissue is then used in the segmentation process. This method using weight maps to fine-tune the result of multi-atlas-based segmentation was tested using a set of 146 atlas images consisting of an MR image of the lower abdomen and a prostate segmentation. Each image served as a target in a set of leave-one-out experiments. These experiments were repeated for a weight map derived from the clinical practice in our hospital. The segmentation accuracy increased 6% in regions that border vulnerable tissue using expert-specified voxel-based weight maps. This was achieved at the cost of a 4% decrease in accuracy in less clinically relevant regions. The inclusion of expert knowledge in a multi-atlas-based segmentation procedure was shown to be feasible for prostate segmentation. This method allows an expert to ensure that automatic segmentation is most accurate in critical regions. This improved local accuracy can increase the practical value of automatic segmentation.

A Distribution-Free Approach to Stochastic Efficiency Measurement with Inclusion of Expert Knowledge

This paper proposes a new efficiency benchmarking methodology that is capable of incorporating probability while still preserving the advantages of a distribution-free and nonparametric modeling technique. This new technique developed in this paper will be known as the DEA-Chebyshev model. The foundation of DEA-Chebyshev model is based on the model pioneered by Charnes, Cooper, and Rhodes in 1978 known as Data Envelopment Analysis (DEA). The combination of normal DEA with DEA-Chebyshev frontier (DCF) can successfully provide a good framework for evaluation based on quantitative data and qualitative intellectual management knowledge. The simulated dataset was tested on DEA-Chebyshev model. It has been statistically shown that this model is effective in predicting a new frontier, whereby DEA efficient units can be further differentiated and ranked. It is an improvement over other methods, as it is easily applied, practical, not computationally intensive, and easy to implement.

Reducing the semantic gap in content-based image retrieval in mammography with relevance feedback and inclusion of expert knowledge

We investigate the use of relevance feedback (RFb) and the inclusion of expert knowledge to reduce the semantic gap in content-based image retrieval (CBIR) of mammograms. Tests were conducted with radiologists, in which their judgment of the relevance of the retrieved images was used with techniques of query-point movement to incorporate RFb. The measures of similarity of images used for CBIR were based upon textural characteristics and the distribution of density of fibroglandular tissue in the breast. The features used include statistics of the gray-level histogram, texture features based upon the gray-level co-occurrence matrix, moment-based features, measures computed in the Radon domain, and granulometric measures. Queries for CBIR with RFb were executed by three radiologists. The performance of CBIR was measured in terms of precision of retrieval and a measure of relevance-weighted precision (RWP) of retrieval. The results indicate improvement due to RFb of up to 62% in precision and 39% in RWP. The gain in performance of CBIR with RFb depended upon the BI-RADS breast density index of the query mammographic image, with greater improvement in cases of mammograms with higher density.

Expert Knowledge in Land Use Planning: The Role of Information in Workshops, Scenario Building, Simulation Modelling and Decision Making

What role does expert information play in the planning process, and how can the role be enhanced by integrating information management and simulation modelling tools into a participatory decision-making exercise? We examine these questions through a survey of pertinent planning literature, and a case study. This paper reports a workshop with expert participants, which developed a set of future scenarios for Portugal's two largest metropolitan areas (Lisbon and Porto, Portugal). A methodology was set up targeting the inclusion of expert knowledge into the modelling process. The research and case study led to three principal conclusions: (1) that it is possible and profitable to include knowledge in decision-making that results from participation from a diversity of experts; (2) that what the experts considered to be the spatial output of their suggestions might be different from simulations that result from modelling; (3) that simulations based on policies that control and constrain the behaviour of urban systems can result in simulation outcomes that were unanticipated by the experts especially in their resultant spatial distributions.

Automated timetabling using an object-oriented scheduler

The timetabling problem has traditionally been treated as a mathematical optimization, heuristic, or human-machine interactive problem. These approaches tend to suffer from lack of flexibility, computational intractability, and poor results. We introduce an automated timetabler that combines data, model and knowledge-bases, developed using object-oriented methodology. The inclusion of expert knowledge allows for solutions that fit the problem context better, while the use of a database enables a more flexible and maintainable system. The object-oriented paradigm allows for a more efficient design and code implementation of scheduling procedures. Results have been promising as the system has automatically scheduled over 2000 students and instructors into more than 300 classes.