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| http://dx.doi.org/10.1016/j.hjdsi.2024.100753 | DOI Listing |
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Mean-field approximation for networks with synchrony-driven adaptive coupling.
Chaos
January 2025
School of Mathematics and Statistics, University College Dublin, Dublin 4 D04 V1W8, Ireland.
Synaptic plasticity plays a fundamental role in neuronal dynamics, governing how connections between neurons evolve in response to experience. In this study, we extend a network model of θ-neuron oscillators to include a realistic form of adaptive plasticity. In place of the less tractable spike-timing-dependent plasticity, we employ recently validated phase-difference-dependent plasticity rules, which adjust coupling strengths based on the relative phases of θ-neuron oscillators.
View Article and Find Full Text PDFGuidelines International Network: Principles for Use of Artificial Intelligence in the Health Guideline Enterprise.
Ann Intern Med
January 2025
Clinical Epidemiology and Research Center (CERC), Department of Biomedical Sciences, Humanitas University, and IRCCS Humanitas Research Hospital, Milan, Italy, and Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Allergology and Immunology, Berlin, Germany (H.J.S.).
Description: Artificial intelligence (AI) has been defined by the High-Level Expert Group on AI of the European Commission as "systems that display intelligent behaviour by analysing their environment and taking actions-with some degree of autonomy-to achieve specific goals." Artificial intelligence has the potential to support guideline planning, development and adaptation, reporting, implementation, impact evaluation, certification, and appraisal of recommendations, which we will refer to as "guideline enterprise." Considering this potential, as well as the lack of guidance for the use of AI in guidelines, the Guidelines International Network (GIN) proposes a set of principles for the development and use of AI tools or processes to support the health guideline enterprise.
View Article and Find Full Text PDFProvider perception of the acceptability, appropriateness, and feasibility of a transdiagnostic intervention in a youth partial hospitalization setting: A mixed methods implementation process evaluation.
Psychol Serv
January 2025
Department of Psychiatry, University of Colorado-Anschutz Medical Campus.
Partial hospitalization programs (PHPs) are increasingly relied upon to provide intensive mental health treatment for youth with acute and severely impairing mental health symptoms, yet very few interventions have been adapted to fit this unique delivery context. Transdiagnostic treatments hold promise for addressing the complex clinical presentations and workflow needs of PHP programs, but more work is needed to understand factors that influence successful implementation. We conducted a formative implementation process evaluation to identify barriers and facilitators of acceptability, appropriateness, and feasibility of implementing an evidence-based transdiagnostic intervention in a PHP setting and further targets for intervention and implementation adaptation.
View Article and Find Full Text PDFImplementing psychosocial interventions for teachers' mental health: Protocol for integrating scoping review with teachers lived experiences in LMICs.
PLoS One
January 2025
Jindal School of Psychology and Counselling, OP Jindal Global University, Sonipat, Haryana, India.
Introduction: Teachers are pivotal in shaping educational environments and student development but face significant occupational stress and high rates of mental problems. Despite the availability of various psychosocial interventions, comprehensive evidence of their effectiveness and implementation is limited for this occupational group, especially in low- and middle-income countries (LMICs). This mixed methods study aims to conduct a scoping review of characteristics, effectiveness, and implementation outcomes of psychosocial interventions for teachers' mental health and mental problems, integrating these with teachers' lived experiences to inform the implementation of mental health interventions in LMICs.
View Article and Find Full Text PDFA safe-enhanced fully closed-loop artificial pancreas controller based on deep reinforcement learning.
PLoS One
January 2025
Institute of Visual Informatics, The National University of Malaysia (UKM), Bangi, Malaysia.
Patients with type 1 diabetes and their physicians have long desired a fully closed-loop artificial pancreas (AP) system that can alleviate the burden of blood glucose regulation. Although deep reinforcement learning (DRL) methods theoretically enable adaptive insulin dosing control, they face numerous challenges, including safety and training efficiency, which have hindered their clinical application. This paper proposes a safe and efficient adaptive insulin delivery controller based on DRL.
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