44 research outputs found
Achieving Business Practicability of Model-Driven Cross-Platform Apps
Due to the incompatibility of mobile device platforms such as Android and iOS, apps have to be developed separately for each target platform. Cross-platform development approaches based on Web technology have significantly improved over the last years. However, since they do not lead to native apps, these frameworks are not feasible for all kinds of business apps. Moreover, the way apps are developed is cumbersome. Advanced cross-platform approaches such as MD2, which is based on model-driven development (MDSD) techniques, are a much more powerful yet less mature choice. We discuss business implications of MDSD for apps and introduce MD2 as our proposed solution to fulfill typical requirements. Moreover, we highlight a business-oriented enhancement that further increases MD2's business practicability. We generalize our findings and sketch the path towards more versatile MDSD of apps
Achieving Business Practicability of Model-Driven Cross-Platform Apps
-Due to the incompatibility of mobile device platforms such as Android and iOS, apps have to be developed separately for each target platform. Cross-platform development approaches based on Web technology have significantly improved over the last years. However, since they do not lead to native apps, these frameworks are not feasible for all kinds of business apps. Moreover, the way apps are developed is cumbersome. Advanced cross-platform approaches such as MD2, which is based on model-driven development (MDSD) techniques, are a much more powerful yet less mature choice. We discuss business implications of MDSD for apps and introduce MD2 as our proposed solution to fulfill typical requirements. Moreover, we highlight a business-oriented enhancement that further increases MD2's business practicability. We generalize our findings and sketch the path towards more versatile MDSD of app
Associations of Health App Use and Perceived Effectiveness in People With Cardiovascular Diseases and Diabetes: Population-Based Survey
Background: Mobile health apps can help to change health-related behaviors and manage chronic conditions in patients with cardiovascular diseases (CVDs) and diabetes mellitus, but a certain level of health literacy and electronic health (eHealth) literacy may be needed. Objective: The aim of this study was to identify factors associated with mobile health app use in individuals with CVD or diabetes and detect relations with the perceived effectiveness of health apps among app users. Methods: The study used population-based Web-based survey (N=1500) among Germans, aged 35 years and older, with CVD, diabetes, or both. A total of 3 subgroups were examined: (1) Individuals with CVD (n=1325), (2) Individuals with diabetes (n=681), and (3) Individuals with CVD and diabetes (n=524). Sociodemographics, health behaviors, CVD, diabetes, health and eHealth literacy, characteristics of health app use, and characteristics of apps themselves were assessed by questionnaires. Linear and logistic regression models were applied. Results: Overall, patterns of factors associated with health app use were comparable in individuals with CVD or diabetes or both. Across subgroups, about every fourth patient reported using apps for health-related purposes, with physical activity and weight loss being the most prominent target behaviors. Health app users were younger, more likely to be female (except in those with CVD and diabetes combined), better educated, and reported more physical activity. App users had higher eHealth literacy than nonusers. Those users who perceived the app to have a greater effectiveness on their health behaviors tended to be more health and eHealth literate and rated the app to use more behavior change techniques (BCTs). Conclusions: There are health- and literacy-related disparities in the access to health app use among patients with CVD, diabetes, or both, which are relevant to specific health care professionals such as endocrinologists, dieticians, cardiologists, or general practitioners. Apps containing more BCTs had a higher perceived effect on people’s health, and app developers should take the complexity of needs into account. Furthermore, eHealth literacy appears to be a requirement to use health apps successfully, which should be considered in health education strategies to improve health in patients with CVD and diabetes
Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks
Brain extraction in magnetic resonance imaging (MRI) data is an important
segmentation step in many neuroimaging preprocessing pipelines. Image
segmentation is one of the research fields in which deep learning had the
biggest impact in recent years enabling high precision segmentation with
minimal compute. Consequently, traditional brain extraction methods are now
being replaced by deep learning-based methods. Here, we used a unique dataset
comprising 568 T1-weighted (T1w) MR images from 191 different studies in
combination with cutting edge deep learning methods to build a fast,
high-precision brain extraction tool called deepbet. deepbet uses LinkNet, a
modern UNet architecture, in a two stage prediction process. This increases its
segmentation performance, setting a novel state-of-the-art performance during
cross-validation with a median Dice score (DSC) of 99.0% on unseen datasets,
outperforming current state of the art models (DSC = 97.8% and DSC = 97.9%).
While current methods are more sensitive to outliers, resulting in Dice scores
as low as 76.5%, deepbet manages to achieve a Dice score of > 96.9% for all
samples. Finally, our model accelerates brain extraction by a factor of ~10
compared to current methods, enabling the processing of one image in ~2 seconds
on low level hardware
DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features
Major depressive disorder (MDD) is a complex psychiatric disorder that
affects the lives of hundreds of millions of individuals around the globe. Even
today, researchers debate if morphological alterations in the brain are linked
to MDD, likely due to the heterogeneity of this disorder. The application of
deep learning tools to neuroimaging data, capable of capturing complex
non-linear patterns, has the potential to provide diagnostic and predictive
biomarkers for MDD. However, previous attempts to demarcate MDD patients and
healthy controls (HC) based on segmented cortical features via linear machine
learning approaches have reported low accuracies. In this study, we used
globally representative data from the ENIGMA-MDD working group containing an
extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a
comprehensive analysis with generalizable results. Based on the hypothesis that
integration of vertex-wise cortical features can improve classification
performance, we evaluated the classification of a DenseNet and a Support Vector
Machine (SVM), with the expectation that the former would outperform the
latter. As we analyzed a multi-site sample, we additionally applied the ComBat
harmonization tool to remove potential nuisance effects of site. We found that
both classifiers exhibited close to chance performance (balanced accuracy
DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher
classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was
found when the cross-validation folds contained subjects from all sites,
indicating site effect. In conclusion, the integration of vertex-wise
morphometric features and the use of the non-linear classifier did not lead to
the differentiability between MDD and HC. Our results support the notion that
MDD classification on this combination of features and classifiers is
unfeasible