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s women, mindfulness training participants had significantly improved depression symptoms compared to nulliparous attention control group participants (group × time interaction χ

=18.1, P=.001).

Smartphone-based mindfulness training is an effective intervention in improving maternal perinatal depression for those who are potentially at risk of perinatal depression in early pregnancy. Nulliparous women are a promising subgroup who may benefit more from mindfulness training.

Chinese Clinical Trial Registry ChiCTR1900028521; http//www.chictr.org.cn/showproj.aspx?proj=33474.

Chinese Clinical Trial Registry ChiCTR1900028521; http//www.chictr.org.cn/showproj.aspx?proj=33474.

Applications of machine learning for the early detection of diseases for which a clear-cut diagnostic gold standard exists have been evaluated. However, little is known about the usefulness of machine learning approaches in the decision-making process for decisions such as insulin initiation by diabetes specialists for which no absolute standards exist in clinical settings.

The objectives of this study were to examine the ability of machine learning models to predict insulin initiation by specialists and whether the machine learning approach could support decision making by general physicians for insulin initiation in patients with type 2 diabetes.

Data from patients prescribed hypoglycemic agents from December 2009 to March 2015 were extracted from diabetes specialists’ registries, resulting in a sample size of 4860 patients who had received initial monotherapy with either insulin (n=293) or noninsulin (n=4567). Neural network output was insulin initiation ranging from 0 to 1 with a cutoff of >0.5 fy is needed before the use of machine learning-based decision support systems for insulin initiation can be incorporated into clinical practice.

The electronic health record (EHR) contains a wealth of medical information. An organized EHR can greatly help doctors treat patients. In some cases, only limited patient information is collected to help doctors make treatment decisions. Because EHRs can serve as a reference for this limited information, doctors’ treatment capabilities can be enhanced. Natural language processing and deep learning methods can help organize and translate EHR information into medical knowledge and experience.

In this study, we aimed to create a model to extract concept embeddings from EHRs for disease pattern retrieval and further classification tasks.

We collected 1,040,989 emergency department visits from the National Taiwan University Hospital Integrated Medical Database and 305,897 samples from the National Hospital and Ambulatory Medical Care Survey Emergency Department data. After data cleansing and preprocessing, the data sets were divided into training, validation, and test sets. We proposed a Transformer-based moperformance. The AUROCs were 0.857, 0.870, and 0.868 for the model without pretraining, the model pretrained by only SimCLR, and the model pretrained by only DIM, respectively. On the smaller finetuning set, the AUROC was 0.815 for the proposed model.

Through contrastive learning methods, disease concepts can be embedded meaningfully. Moreover, these methods can be used for disease retrieval tasks to enhance clinical practice capabilities. The disease concept model is also suitable as a pretrained model for subsequent prediction tasks.

Through contrastive learning methods, disease concepts can be embedded meaningfully. this website Moreover, these methods can be used for disease retrieval tasks to enhance clinical practice capabilities. The disease concept model is also suitable as a pretrained model for subsequent prediction tasks.

Early adolescent unintended pregnancy and sexually transmitted infection prevention are significant public health challenges in the United States. Parental influence can help adolescents make responsible and informed sexual health decisions toward delayed sexual debut; yet parents often feel ill equipped to communicate about sex-related topics. Intergenerational games offer a potential strategy to provide life skills training to young adolescents (aged 11-14 years) while engaging them and their parents in communication about sexual health.

This study aims to describe the development of a web-based online sexual health intergenerational adventure game, the Secret of Seven Stones (SSS), using an intervention mapping (IM) approach for developing theory- and evidence-based interventions.

We followed the IM development steps to describe a theoretical and empirical model for young adolescent sexual health behavior, define target behaviors and change objectives, identify theory-based methods and practical applorough approach to development and testing, attentive to the need for theoretical and empirical foundations in serious games for health.

The SSS intergenerational game provides a unique addition to the limited cadre of home-based programs that facilitate parent involvement in influencing young adolescent behaviors and reducing adolescent sexual risk taking. The IM framework provided a logical and thorough approach to development and testing, attentive to the need for theoretical and empirical foundations in serious games for health.

Prolonged sedentary behavior is related to a number of risk factors for chronic diseases. Given the high prevalence of sedentary behavior in daily life, simple yet practical solutions for behavior change are needed to avoid detrimental health effects.

The mobile app SedVis was developed based on the health action process approach. The app provides personal mobility pattern visualization (for both physical activity and sedentary behavior) and action planning for sedentary behavior change. The primary aim of the study is to investigate the effect of mobility pattern visualization on users’ action planning for changing their sedentary behavior. The secondary aim is to evaluate user engagement with the visualization and user experience of the app.

A 3-week user study was conducted with 16 participants who had the motivation to reduce their sedentary behavior. Participants were allocated to either an active control group (n=8) or an intervention group (n=8). In the 1-week baseline period, none of the participants had access to the functions in the app.