Data-Driven Audiogram Classification for Mobile Audiometry

Recent mobile and automated audiometry technologies have allowed for the democratization of hearing healthcare and enables non-experts to deliver hearing tests. The problem remains that a large number of such users are not trained to interpret audiograms. In this work, we outline the development of a data-driven audiogram classification system designed specifically for the purpose of concisely describing audiograms. More specifically, we present how a training dataset was assembled and the development of the classification system leveraging supervised learning techniques. We show that three practicing audiologists had high intra- and inter-rater agreement over audiogram classification tasks pertaining to audiogram configuration, symmetry and severity. The system proposed here achieves a performance comparable to the state of the art, but is signific

Comparaison de Deux Types de Bobinage d'une MSAP pour la Propulsion d'un Véhicule Hybride Urbain

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Mining Audiograms to Improve the Interpretability of Automated Audiometry Measurements

Many people with hearing loss are unaware of it and do not seek benefit from available interventions such as hearing aids. This is in part due to the limited accessibility to qualified hearing healthcare providers in developing and developed countries alike. Automated audiometry, which has gained in popularity amidst the torrent of advances in telemedicine and mobile health, makes it possible to deliver basic hearing tests to remote or otherwise underserved communities at low cost. While this technology makes it possible to perform hearing assessments outside of a sound booth, many individuals administering the test are non-specialists, and thus, have a limited ability to interpret audiometric measurements and to make tailored recommendations. In this paper, we present the first steps towards the development of a flexible, supervised learning approach for the classification of audiograms in terms of their shape, severity and symmetry. More specifically, we outline our approach to building a set of non-redundant, annotation-ready audiograms from a much larger dataset. In addition, we present a Rapid Audiogram Annotation Environment (RAAE) designed specifically for the collection of audiogram annotations from a large community of expert audiologists. Preliminary results indicate that annotations provided through our environment are consistent leading to low intra-coder variability. Data gathered through the RAAE will form the basis of learning algorithms to help non-experts make better dec

Applications of Machine Learning Methods in Retrospective Studies on Hearing

Hearing healthcare professionals rely on the audiograms produced through pure tone audiometry, among other tests, to diagnose and treat hearing loss. Researchers also rely on audiograms to study the prevalence of hearing loss in various populations. Notably, due to the available test time, intraoctave frequencies are not often recorded, even though they can contribute to certain diagnoses. Previous work has proposed the imputation of these thresholds using a simple average of neighboring thresholds. In this work, we present an alternative approach for addressing missing intra-octave thresholds that relies on a $\pmb{k}$ -nearest neighbors algorithm and show that accuracy can be slightly improved using a data-driven approach to imputation. We also present a Gaussian mixture model-based approach to flagging atypical or potentially unreliable audiograms to produce high quality datasets. Our method allows the imputation of intra-octave thresholds with an accuracy no worse than simple averaging. For the more challenging 6000 Hz threshold, our method appears to be particularly effective. Overall, our method allows for improved presentation of complete audiogram datasets

Capability model to improve infrastructure asset performance

Infrastructure organizations are operating in an increasingly challenging business environment as a result of globalization, privatization and deregulation. In an external business environment that is constantly changing, extant literature on strategic management advocates the need to focus on factors internal to the organization such as resources and capabilities to sustain their performance. Specifically, they need to develop dynamic capabilities in order to survive and prosper under conditions of change. The aim of this paper is to explore the dynamic capabilities needed in the management of transport infrastructure assets using a multiple case study research strategy. This paper produced a number of findings. First, the empirical evidence showed that the core infrastructure asset management processes are capacity management, options evaluation, procurement & delivery, maintenance management, and asset information management. Second, the study identified five dynamic capabilities namely stakeholder connectivity, cross-functional, relational, technology absorptive and integrated information capability as central to executing the strategic infrastructure asset management processes well. These findings culminate in the development of a capability model to improve the performance of infrastructure assets in an increasingly dynamic business environment

Identifying, Enabling and Managing Dynamic Capabilities in the Public Sector

In this paper, we examine how a public sector organization developed a new strategic approach based on the identification and use of an internal dynamic capability (learning through experimenting). In response to the need for continual performance improvement in spite of reduced financial resources, this organization engaged in three overlapping phases as they shifted to this strategic approach. First, managers identified appropriate latent dynamic capabilities. Next, they used their leadership skills and built on established levels of trust to enable the use of these dynamic capabilities. Finally, they managed the tension between unrestricted development of local initiatives and organizational needs for guidance and control. Copyright Blackwell Publishing Ltd 2007.

Proteome-wide Prediction of Lysine Methylation Leads to Identification of H2BK43 Methylation and Outlines the Potential Methyllysine Proteome

Protein Lys methylation plays a critical role in numerous cellular processes, but it is challenging to identify Lys methylation in a systematic manner. Here we present an approach combining in silico prediction with targeted mass spectrometry (MS) to identify Lys methylation (Kme) sites at the proteome level. We develop MethylSight, a program that predicts Kme events solely on the physicochemical properties of residues surrounding the putative methylation sites, which then requires validation by targeted MS. Using this approach, we identify 70 new histone Kme marks with a 90% validation rate. H2BK43me2, which undergoes dynamic changes during stem cell differentiation, is found to be a substrate of KDM5b. Furthermore, MethylSight predicts that Lys methylation is a prevalent post-translational modification in the human proteome. Our work provides a useful resource for guiding systematic exploration of the role of Lys methylation in human health and disease.Biggar et al. develop an algorithm to identify lysine methylation sites and use this resource to provide insight into the potential of the methyllysine proteome. The results also validate 45 new histone methylation sites by targeted mass spectrometry and show that one of these sites, H2B-K43me2, is a substrate of the KDM5B demethylase