Exploring the Boundaries of Conditionality in the EU. Egmont European Policy Brief No. 51 June 2018

Conditionality in the EU comes in many forms: legally codified and enforced by the Court of Justice, or reliant on intergovernmental bargaining and expressed by means of political or economic (dis)incentives. This European Policy Brief explores the boundaries of the conditionality debate, and assesses what varying degrees of conditionality can and cannot achieve. The overarching objective of conditionality is to foster integration and cohesion amongst the peoples of Europe and their Member States. A sound logic of conditionality must therefore set incentives in such a way that their application contributes to this intended outcome. A balanced combination of political, legal and budgetary instruments can help remedy a major lacuna in the Treaties: the effective protection of the rule of law and democracy

Feature engineering for ICU mortality prediction based on hourly to bi-hourly measurements

Mortality prediction for intensive care unit (ICU) patients is a challenging problem that requires extracting discriminative and informative features. This study presents a proof of concept for exploring features that can provide clinical insight. Through a feature engineering approach, it is attempted to improve ICU mortality prediction in field conditions with low frequently measured data (i.e., hourly to bi-hourly). Features are explored by investigating the vital signs measurements of ICU patients, labelled with mortality or survival at discharge. The vital signs of interest in this study are heart and respiration rate, oxygen saturation and blood pressure. The latter comprises systolic, diastolic and mean arterial pressure. In the feature exploration process, it is aimed to extract simple and interpretable features that can provide clinical insight. For this purpose, a classifier is required that maximises the margin between the two classes (i.e., survival and mortality) with minimum tolerance to misclassification errors. Moreover, it preferably has to provide a linear decision surface in the original feature space without mapping to an unlimited dimensionality feature space. Therefore, a linear hard margin support vector machine (SVM) classifier is suggested. The extracted features are grouped in three categories: statistical, dynamic and physiological. Each category plays an important role in enhancing classification error performance. After extracting several features within the three categories, a manual feature fine-tuning is applied to consider only the most efficient features. The final classification, considering mortality as the positive class, resulted in an accuracy of 91.56%, sensitivity of 90.59%, precision of 86.52% and F-1-score of 88.50%. The obtained results show that the proposed feature engineering approach and the extracted features are valid to be considered and further enhanced for the mortality prediction purpose. Moreover, the proposed feature engineering approach moved the modelling methodology from black-box modelling to grey-box modelling in combination with the powerful classifier of SVMs

Optimizing the use of InSAR observations in data assimilation problems to estimate reservoir compaction

Hydrocarbon production may cause subsidence as a result of the pressure reduction in the gas-producing layer and reservoir compaction. To analyze the process of subsidence and estimate reservoir parameters, we use a particle method to assimilate Interferometric synthetic-aperture radar (InSAR) observations of surface deformation with a conceptual model of reservoir. As example, we use an analytical model of the Groningen gas reservoir based on a geometry representing the compartmentalized structure of the subsurface at the reservoir depth. The efficacy of the particle method becomes less when the degree of freedom is large compared to the ensemble size. This degree of freedom, in turn, varies because of spatial correlation in the observed field. The resolution of the InSAR data and the number of observations affect the performance of the particle method. In this study, we quantify the information in a Sentinel-1 SAR dataset using the concept of Shannon entropy from information theory. We investigate how to best capture the level of detail in model resolved by the InSAR data while maximizing their information content for a data assimilation use. We show that incorrect representation of the existing correlations leads to weight collapse when the number of observation increases, unless the ensemble size growths. However, simulations of mutual information show that we could optimize data reduction by choosing an adequate mesh given the spatial correlation in the observed subsidence. Our approach provides a means to achieve a better information use from available InSAR data reducing weight collapse without additional computational cost

Vital signs prediction and early warning score calculation based on continuous monitoring of hospitalised patients using wearable technology

In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients’ vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the EWS for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of kNN-LS-SVM for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of EWS in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of LSTM. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of EWS in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate EWS computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration

Design and structure of GraphoGame-Flemish, an app-based tool to support early reading acquisition

Digital games have shown to have promising potential as support tools for early reading acquisition in children at risk for or diagnosed with dyslexia. Detailed technical descriptions of these tools are essential to enable comparison of treatment effects across studies with different game content, study designs and target languages. It also helps to uncover which aspects of game-based learning are driving its effectiveness. GraphoGame is a child-friendly computerized tool to support reading acquisition in beginning readers by systematically and explicitly training grapheme-phoneme correspondences. This work provides an in-depth description of the design and structure of a Flemish version of GraphoGame which was created as a preventive intervention for Flemish Dutch-speaking pre-readers at cognitive risk for dyslexia. We designed, envisioned, implemented and piloted a range of new tasks suitable for this aim in this young target population. A lot more weight was given to grapheme and phoneme identification and discrimination which are often overlooked in the training of grapheme-phoneme associations at the core of most other versions of GraphoGame. A cohort of 62 pre-reading children at cognitive risk for dyslexia (average age 5.5 years) trained with GraphoGame-Flemish on average 66 times during a 90-day intervention period. After the playing phase, more than half of these pre-readers reported that the content they played just before the end of the intervention was challenging for them. Nevertheless, they still managed to sustain a median exposure of 15 minutes per playing session over the entire three-month period and given that no child mastered all game levels, GraphoGame-Flemish has the potential to engage at-risk children at the onset of literacy in training their early-reading skills over an extended period of time

GraphoGame Flemish: a computerized literacy training for children at cognitive risk for dyslexia

Posterstatus: publishe

Feasibility, enjoyment, and language comprehension impact of a tablet- and GameFlow-based story-listening game for kindergarteners: Methodological and mixed methods study

Background: Enjoyment plays a key role in the success and feasibility of serious gaming interventions. Unenjoyable games will not be played and in the case of serious gaming, learning will not occur. Given this importance, a so-called GameFlow model has been developed, intending to guide (serious) game developers in the process of creating and evaluating enjoyment in digital (serious) games. Regarding language learning, a variety of serious games, targeting specific language components, exist on the market, albeit often without available assessments of enjoyment or feasibility. Objective: The current study evaluates enjoyment and feasibility of a tablet-based serious story listening game for kindergarteners, developed based on the principles of the GameFlow model. Additionally, this study preliminarily explores the possibility of the game to foster language comprehension. Methods: Within the framework of a broader preventive reading intervention, 91 five-year old kindergarteners at cognitive risk for dyslexia were asked to play the story game for 12 weeks, six days per week, either combined with a tablet-based phonics intervention or active control games. The story game mainly involved story listening and rating, and responding to content-related questions. Game enjoyment was assessed via post-intervention questionnaires, a GameFlow-based evaluation, and in-game story rating data. Feasibility was determined based on in-game general question response accuracy (QRA), reflecting the difficulty level, attrition rate, and final game exposure and training duration. Additionally, in order to investigate whether game enjoyment and difficulty could influence feasibility, final game exposure and training duration were predicted based on in-game initial story ratings and initial QRA. The possible growth in language comprehension was explored by analyzing in-game QRA as a function of game phase and baseline language skills. Results: Eventually, data from 82 participants were analyzed. Questionnaire and in-game data suggested an overall enjoyable game experience, but the GameFlow-based evaluation implied room for improving the game design. General QRA confirmed a well-adapted difficulty level for the target sample. Moreover, despite an overall attrition rate of 32 participants (39%), 74 participants (90%) still completed 80% of the game, albeit with a large variation in training days. Higher initial QRA resulted in a significantly higher game exposure (β=0.35; P<.001) and lower initial story ratings significantly slackened the training duration (β=-0.16; P=.003). In-game QRA was positively predicted by game phase (β=1.44; P=.004), baseline listening comprehension (β=1.56; P=.002), and vocabulary (β=0.16; P=.01), with larger QRA growths over game phases in children with lower baseline listening comprehension skills (β=-0.08; P=.04). Conclusions: Generally, participants experienced the story game as enjoyable and feasible. Yet, the GameFlow-model evaluation and predictive relationships imply room for further game design improvement. Furthermore, our results cautiously suggest a potential of the game to foster language comprehension, but future randomized controlled trials need to elucidate the actual gaming impact on language comprehension

Speech perception deficits and the effect of envelope-enhanced story listening combined with phonics intervention in pre-readers at risk for dyslexia

Developmental dyslexia is considered to be most effectively addressed with preventive phonics-based interventions, including grapheme-phoneme coupling and blending exercises. These intervention types require intact speech perception abilities, given their large focus on exercises with auditorily presented phonemes. Yet some children with (a risk for) dyslexia experience problems in this domain due to a poorer sensitivity to rise times, i.e., rhythmic acoustic cues present in the speech envelope. As a result, the often subtle speech perception problems could potentially constrain an optimal response to phonics-based interventions in at-risk children. The current study therefore aimed (1) to extend existing research by examining the presence of potential speech perception deficits in pre-readers at cognitive risk for dyslexia when compared to typically developing peers and (2) to explore the added value of a preventive auditory intervention for at-risk pre-readers, targeting rise time sensitivity, on speech perception and other reading-related skills. To obtain the first research objective, we longitudinally compared speech-in-noise perception between 28 5-year-old pre-readers with and 30 peers without a cognitive risk for dyslexia during the second half of the third year of kindergarten. The second research objective was addressed by exploring growth in speech perception and other reading-related skills in an independent sample of 62 at-risk 5-year-old pre-readers who all combined a 12-week preventive phonics-based intervention (GraphoGame-Flemish) with an auditory story listening intervention. In half of the sample, story recordings contained artificially enhanced rise times (GG-FL_EE group, n = 31), while in the other half, stories remained unprocessed (GG-FL_NE group, n = 31; Clinical Trial Number S60962—https://www.uzleuven.be/nl/clinical-trial-center). Results revealed a slower speech-in-noise perception growth in the at-risk compared to the non-at-risk group, due to an emerged deficit at the end of kindergarten. Concerning the auditory intervention effects, both intervention groups showed equal growth in speech-in-noise perception and other reading-related skills, suggesting no boost of envelope-enhanced story listening on top of the effect of combining GraphoGame-Flemish with listening to unprocessed stories. These findings thus provide evidence for a link between speech perception problems and dyslexia, yet do not support the potential of the auditory intervention in its current form

Preventive Intervention Aids Auditory Perception in Pre Readers at Risk for Dyslexia

status: publishe

Pre-literacy heterogeneity in Dutch-speaking kindergartners: latent profile analysis

Research demonstrated that a dyslexia diagnosis is mainly given after the most effective time for intervention has passed, referred to as the dyslexia paradox. Although some pre-reading cognitive measures have been found to be strong predictors of early literacy acquisition, i.e., phonological awareness (PA), letter knowledge (LK), and rapid automatized naming (RAN), more insight in the variability of pre-reading profiles might be of great importance for early identification of children who have an elevated risk for developing dyslexia and to provide tailor-made interventions. To address this issue, this study used a latent profile analysis (LPA) to disentangle different pre-reading profiles in a sample of 1091 Dutch-speaking kindergartners. Four profiles emerged: high performers (16.50%), average performers (40.24%), below-average performers with average IQ (25.57%), and below-average performers with below-average IQ (17.69%). These results suggested two at-risk profiles diverging in IQ, which are presumably more likely to develop dyslexia later on. Although below-average profiles differed significantly in rapid naming and IQ, no clear evidence for the double-deficit theory was found in Dutch-speaking kindergartners. Educational level and reading history of the parents appeared to be predictive for children's classification membership. Our results point towards the heterogeneity that is already present in kindergartners and the possibility to identify at-risk profiles prior to reading instruction, which may be the foundation for earlier targeted interventions. However, more extended research is needed to determine the stability of these profiles across time and across different languages.status: publishe