Horizons and Perspectives eHealth

EHealth platform represents the combined use of IT technologies and electronic communications in the health field, using data (electronically transmitted, stored and accessed) with a clinical, educational and administrative purpose, both locally and distantly. eHealth has the significant capability to increase the movement in the direction of services centered towards citizens, improving the quality of the medical act, integrating the application of Medical Informatics (Medical IT), Telemedicine, Health Telematics, Telehealth, Biomedical engineering and Bioinformatics. Supporting the creation, development and recognition of a specific eHealth zone, the European Union policies develop through its programs FP6 and FP7, European-scale projects in the medical information technologies (the electronic health cards, online medical care, medical web portals, trans-European nets for medical information, biotechnology, generic instruments and medical technologies for health, ICT mobile systems for remote monitoring). The medical applications like electronic health cards ePrescription, eServices, medical eLearning, eSupervision, eAdministration are integral part of what is the new medical branch-eHealth, being in a continuous expansion due to the support from the global political, financial and medical organizations; the degree of implementation of the eHealth platform varying according to the development level of the communication infrastructure, allocated funds, intensive political priorities and governmental organizations opened to the new IT challenges.eHealth, telemedicine, telehealth, bioinformatics, telematics

MissForest - nonparametric missing value imputation for mixed-type data

Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data the different types are usually handled separately. Therefore, these methods ignore possible relations between variable types. We propose a nonparametric method which can cope with different types of variables simultaneously. We compare several state of the art methods for the imputation of missing values. We propose and evaluate an iterative imputation method (missForest) based on a random forest. By averaging over many unpruned classification or regression trees random forest intrinsically constitutes a multiple imputation scheme. Using the built-in out-of-bag error estimates of random forest we are able to estimate the imputation error without the need of a test set. Evaluation is performed on multiple data sets coming from a diverse selection of biological fields with artificially introduced missing values ranging from 10% to 30%. We show that missForest can successfully handle missing values, particularly in data sets including different types of variables. In our comparative study missForest outperforms other methods of imputation especially in data settings where complex interactions and nonlinear relations are suspected. The out-of-bag imputation error estimates of missForest prove to be adequate in all settings. Additionally, missForest exhibits attractive computational efficiency and can cope with high-dimensional data.Comment: Submitted to Oxford Journal's Bioinformatics on 3rd of May 201

New Interactions with Workflow Systems

This paper describes the evaluation of our early design ideas of an ad-hoc of workflow system. Using the teach-back technique, we have performed a hermeneutic analysis of the mockup implementation named NIWS to get corrective and creative feedback at the functional, dialogue and representation level of the new workflow system

Novel Multimodal Feedback Techniques for In-Car Mid-Air Gesture Interaction

This paper presents an investigation into the effects of different feedback modalities on mid-air gesture interaction for infotainment systems in cars. Car crashes and near-crash events are most commonly caused by driver distraction. Mid-air interaction is a way of reducing driver distraction by reducing visual demand from infotainment. Despite a range of available modalities, feedback in mid-air gesture systems is generally provided through visual displays. We conducted a simulated driving study to investigate how different types of multimodal feedback can support in-air gestures. The effects of different feedback modalities on eye gaze behaviour, and the driving and gesturing tasks are considered. We found that feedback modality influenced gesturing behaviour. However, drivers corrected falsely executed gestures more often in non-visual conditions. Our findings show that non-visual feedback can reduce visual distraction significantl

FoxP2 isoforms delineate spatiotemporal transcriptional networks for vocal learning in the zebra finch.

Human speech is one of the few examples of vocal learning among mammals yet ~half of avian species exhibit this ability. Its neurogenetic basis is largely unknown beyond a shared requirement for FoxP2 in both humans and zebra finches. We manipulated FoxP2 isoforms in Area X, a song-specific region of the avian striatopallidum analogous to human anterior striatum, during a critical period for song development. We delineate, for the first time, unique contributions of each isoform to vocal learning. Weighted gene coexpression network analysis of RNA-seq data revealed gene modules correlated to singing, learning, or vocal variability. Coexpression related to singing was found in juvenile and adult Area X whereas coexpression correlated to learning was unique to juveniles. The confluence of learning and singing coexpression in juvenile Area X may underscore molecular processes that drive vocal learning in young zebra finches and, by analogy, humans