The Learning Health System (LHS) describes linking routine healthcare systems directly with both research translation and knowledge translation as an extension of the evidence-based medicine paradigm, taking advantage of the ubiquitous use of electronic health record (EHR) systems. TRANSFoRm is an EU FP7 project that seeks to develop an infrastructure for the LHS in European primary care. Methods. The project is based on three clinical use cases, a genotype-phenotype study in diabetes, a randomised controlled trial with gastroesophageal reflux disease, and a diagnostic decision support system for chest pain, abdominal pain, and shortness of breath. Results. Four models were developed (clinical research, clinical data, provenance, and diagnosis) that form the basis of the projects approach to interoperability. These models are maintained as ontologies with binding of terms to define precise data elements. CDISC ODM and SDM standards are extended using an archetype approach to enable a two-level model of individual data elements, representing both research content and clinical content. Separate configurations of the TRANSFoRm tools serve each use case. Conclusions. The project has been successful in using ontologies and archetypes to develop a highly flexible solution to the problem of heterogeneity of data sources presented by the LHS.
PurposeBaseline ocular surface characteristics in children require investigation. This study characterised blinking and relationships with ocular symptoms, tear film and digital device use.Methods45 children aged 6–15 years (56% female) participated in a cross-sectional study. Ocular surface symptoms (Instant Ocular Symptoms Survey, Dry Eye Questionnaire 5, Symptoms Assessment in Dry Eye, Ocular Surface Disease Index, Ocular Comfort Index and Numerical Rating Scale) and clinical indices (lipid layer thickness, tear secretion and stability, meibomian gland) were assessed. Blink rate and interblink interval were measured in situ using a wearable eye-tracking headset (Pupil Labs GmbH, Germany). Associations between blinking, ocular surface, age, and digital device use (bivariate and partial correlations) and between automated and manually counted blink rate (Bland & Altman) were examined.ResultsMean blink rate and interblink interval were 20.5±10.5 blinks/min and 2.9±1.9 s during conversation. There was no difference between automated and manual blink rate (p=0.78) and no relationship between blinking and digital device use, age or sex. Mean group symptoms were within normal range and not associated with clinical measurements including blinking. Greater tear volume was associated with a faster blink rate (r=0.46, p=0.001) and shorter interblink interval (r=−0.36, p=0.02). Older age was associated with improved tear volume (r=0.37, p=0.01) and stability (r=0.38, p=0.01).ConclusionsBlinking characterised in situ was not impacted by age or habitual digital device use. A faster blink rate was associated with greater tear volume but not symptoms. Improved tear function was found with age suggesting that the ocular surface continues to develop through childhood.
This paper presents and evaluates a system and method that record spatiotemporal scene information and location of the center of visual attention, i.e., spatiotemporal point of regard (PoR) in ecological environments. A primary research application of the proposed system and method is for enhancing current 2D visual attention models. Current eye-tracking approaches collapse a scene’s depth structures to a 2D image, omitting visual cues that trigger important functions of the human visual system (e.g., accommodation and vergence). We combined head-mounted eye-tracking with a miniature time-of-flight camera to produce a system that could be used to estimate the spatiotemporal location of the PoR—the point of highest visual attention—within 3D scene layouts. Maintaining calibration accuracy is a primary challenge for gaze mapping; hence, we measured accuracy repeatedly by matching the PoR to fixated targets arranged within a range of working distances in depth. Accuracy was estimated as the deviation from estimated PoR relative to known locations of scene targets. We found that estimates of 3D PoR had an overall accuracy of approximately 2° omnidirectional mean average error (OMAE) with variation over a 1 h recording maintained within 3.6° OMAE. This method can be used to determine accommodation and vergence cues of the human visual system continuously within habitual environments, including everyday applications (e.g., use of hand-held devices).
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