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Mobile Health Intervention Tools Promoting HIV Pre-Exposure Prophylaxis Among Adolescent Girls and Young Women in Sub-Saharan Africa: Scoping Review

Mobile Health Intervention Tools Promoting HIV Pre-Exposure Prophylaxis Among Adolescent Girls and Young Women in Sub-Saharan Africa: Scoping Review

Our literature search identified a number of m Health interventions, such as B-wise [80], a website with Pr EP information for adolescent girls and young women in South Africa, and Tutu Tester [81], a mobile clinic that used m Health to connect with HIV testers in the community [82,83]; however, these interventions were not included, as their impacts from m Health were not published in peer-reviewed journals or as conference abstracts.

Alex Emilio Fischer, Homaira Hanif, Jacob B Stocks, Aimee E Rochelle, Karen Dominguez, Eliana Gabriela Armora Langoni, H Luz McNaughton Reyes, Gustavo F Doncel, Kathryn E Muessig

JMIR Mhealth Uhealth 2025;13:e60819

Causal AI Recommendation System for Digital Mental Health: Bayesian Decision-Theoretic Analysis

Causal AI Recommendation System for Digital Mental Health: Bayesian Decision-Theoretic Analysis

For example, consider a set of variables {A,B,C,D} that have the dependency relations described by the DAG A←B→C→D, then this DAG would be consistent with the partition {{B},{A,C},{D}}. The sampling procedure was run across 8 chains and checked for convergence and resolution (Note S2 in Multimedia Appendix 1). We used the Bayesian Gaussian equivalent score to retain the ordinal information of the random variables. Simulating outcomes given a DAG is performed by constructing a Bayesian network (BN).

Mathew Varidel, Victor An, Ian B Hickie, Sally Cripps, Roman Marchant, Jan Scott, Jacob J Crouse, Adam Poulsen, Bridianne O'Dea, Sarah McKenna, Frank Iorfino

J Med Internet Res 2025;27:e71305

Development of Machine Learning–Based Risk Prediction Models to Predict Rapid Weight Gain in Infants: Analysis of Seven Cohorts

Development of Machine Learning–Based Risk Prediction Models to Predict Rapid Weight Gain in Infants: Analysis of Seven Cohorts

(B) Model 2 included model 1 predictors plus any breastfeeding (yes or no) at age 6 months. (C) Model 3 included model 2 predictors plus solids introduction (yes or no) at age 6 months. AUC: area under the receiver operating characteristic curve; MLP: multilayer perceptron; SVC: support vector classifier. Precision recall curves (PRC) of 8 machine learning algorithms in predicting infants at risk of rapid weight gain by the age of 1 year in the training dataset (internal validation).

Miaobing Zheng, Yuxin Zhang, Rachel A Laws, Peter Vuillermin, Jodie Dodd, Li Ming Wen, Louise A Baur, Rachael Taylor, Rebecca Byrne, Anne-Louise Ponsonby, Kylie D Hesketh

JMIR Public Health Surveill 2025;11:e69220