Decision support system to help choose between an ITE or a BTE hearing aid

A decision support system (DSS) is used for analysing a situation and making decisions. The goal of this research is to mine a large set of heterogeneous audiology data and create a DSS to help audiology technicians to choose between an ITE or BTE hearing aid. Although, in many cases such a choice is clear cut, but at other times this system could be used as a second opinion to predict the hearing aid type. A number of data mining techniques, such as clustering of audiograms, association analysis of variables (such as, age, gender, diagnosis, masker, mould and free text keywords) using contingency tables and principal component analysis on audiograms were used to find candidate variables to be combined into a DSS. The DSS was created using the techniques of logistic regression, Naïve Bayesian analysis and Bayesian networks, and these systems were tested and validated on test data to see which of the techniques produced the better results. This DSS takes air and bone conduction frequencies, age, gender, diagnosis, masker, mould and some free text words associated with a patient as input and gives as the output a decision as to whether the patient would be more likely to prefer an ITE or a BTE hearing aid type. The highest agreement between predicted results and actual hearing aid type in the data were obtained using Bayesian networks, with 93 to 94 percent similarity overall, with a precision of 0.91 for ITE and 0.96 for BTE. The reason for this might be that the Bayesian network also considers interaction between variables while the other two techniques (logistic regression and Naïve Bayesian analysis) consider only the individual variables. One of the important features of this DSS is that once the final choice of hearing aid type is predicted, the decision process can be tracked back to see which factors (variables) contributed how much to the final decision. The theoretical upper bound of classifier performance is the inter-annotator agreement (Altman, 1991), in this case the rate at which two expert audiologists would assign the same hearing aid to the same patient. Unfortunately, this type of data was not included in the audiology database

Online Dectection and Modeling of Safety Boundaries for Aerospace Application Using Bayesian Statistics

The behavior of complex aerospace systems is governed by numerous parameters. For safety analysis it is important to understand how the system behaves with respect to these parameter values. In particular, understanding the boundaries between safe and unsafe regions is of major importance. In this paper, we describe a hierarchical Bayesian statistical modeling approach for the online detection and characterization of such boundaries. Our method for classification with active learning uses a particle filter-based model and a boundary-aware metric for best performance. From a library of candidate shapes incorporated with domain expert knowledge, the location and parameters of the boundaries are estimated using advanced Bayesian modeling techniques. The results of our boundary analysis are then provided in a form understandable by the domain expert. We illustrate our approach using a simulation model of a NASA neuro-adaptive flight control system, as well as a system for the detection of separation violations in the terminal airspace

Expert Elicitation for Reliable System Design

This paper reviews the role of expert judgement to support reliability assessments within the systems engineering design process. Generic design processes are described to give the context and a discussion is given about the nature of the reliability assessments required in the different systems engineering phases. It is argued that, as far as meeting reliability requirements is concerned, the whole design process is more akin to a statistical control process than to a straightforward statistical problem of assessing an unknown distribution. This leads to features of the expert judgement problem in the design context which are substantially different from those seen, for example, in risk assessment. In particular, the role of experts in problem structuring and in developing failure mitigation options is much more prominent, and there is a need to take into account the reliability potential for future mitigation measures downstream in the system life cycle. An overview is given of the stakeholders typically involved in large scale systems engineering design projects, and this is used to argue the need for methods that expose potential judgemental biases in order to generate analyses that can be said to provide rational consensus about uncertainties. Finally, a number of key points are developed with the aim of moving toward a framework that provides a holistic method for tracking reliability assessment through the design process.Comment: This paper commented in: [arXiv:0708.0285], [arXiv:0708.0287], [arXiv:0708.0288]. Rejoinder in [arXiv:0708.0293]. Published at http://dx.doi.org/10.1214/088342306000000510 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Comment: Expert Elicitation for Reliable System Design

Comment: Expert Elicitation for Reliable System Design [arXiv:0708.0279]Comment: Published at http://dx.doi.org/10.1214/088342306000000529 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Comment: Expert Elicitation for Reliable System Design

Comment: Expert Elicitation for Reliable System Design [arXiv:0708.0279]Comment: Published at http://dx.doi.org/10.1214/088342306000000547 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org