Determinants of Health Seeking Behavior among Caregivers of Infants Admitted with Acute Childhood Illnesses at Kenyatta National Hospital, Nairobi, Kenya by Winfred Muringi Wambui, Samuel Kimani, Eunice Odhiambo

“…Provision of a seamless supply system, infrastructural support, and technical support for soft skills minimize the turnaround time which is critical.…”
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Machine learning models for predicting interaction affinity energy between human serum proteins and hemodialysis membrane materials by Simin Nazari, Amira Abdelrasoul

“…A comparative analysis of linear regression, K-nearest neighbors regression, decision tree regression, random forest regression, XGBoost regression, lasso regression, and support vector regression is conducted to predict affinity energy. …”
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Predictive factors for the development of depression in children and adolescents: a clinical study by Hong Zhang, Peilin Yu, Xiaoming Liu, Xiaoming Liu, Ke Wang, Ke Wang, Ke Wang, Ke Wang

“…Also, the DCA and calibration curves demonstrate excellent performance.ConclusionValidated nomogram can accurately predict the risk of depression in children and adolescents, providing clues for clinical practitioners to develop targeted interventions and support.…”
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Understanding Electric Bikers’ Red-Light Running Behavior: Predictive Utility of Theory of Planned Behavior vs Prototype Willingness Model by Tianpei Tang, Hua Wang, Xizhao Zhou, Hao Gong

“…Our findings also support designing of more effective behavior-change interventions to better target e-bikers’ RLR behavior by considering the influence of the identified psychological factors.…”
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Exploring the quality of end-of-life care in the intensive care unit: a qualitative multiple case study approach with family members of Muslim patients by Robert Fowler, Craig Dale, Ayah Nayfeh, Lesley Gotlib Conn

“…Four central themes were identified: (1) trust and confidence in the healthcare team overseen by medical experts; (2) quality communication with medical experts; (3) achieving patient goals of care; and (4) dignity of care through cultural respect and emotional support. Culture, religion and religiosity did not appear to have a major influence on the medical decision-making process. …”
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Opioid Nonadherence Risk Prediction of Patients with Cancer-Related Pain Based on Five Machine Learning Algorithms by Jinmei Liu, Juan Luo, Xu Chen, Jiyi Xie, Cong Wang, Hanxiang Wang, Qi Yuan, Shijun Li, Yu Zhang, Jianli Hu, Chen Shi

“…The DCA showed that clinical interventions for patients at high risk of opioid nonadherence based on the LR model can benefit patients. …”
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Community engagement initiatives in primary health care to achieve universal health coverage: A realist synthesis of scoping review. by Daniel Erku, Resham Khatri, Aklilu Endalamaw, Eskinder Wolka, Frehiwot Nigatu, Anteneh Zewdie, Yibeltal Assefa

“…<h4>Results</h4>Themes related to the structural aspects of CE initiatives included the methodological approaches (i.e., format and composition), levels of CE (i.e., extent, time, and timing of engagement) and the support processes and strategies (i.e., skills and capacity) that are put in place to enable both communities and service providers to undertake successful CE. …”
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An interpretable machine learning tool for in-home monitoring of agitation episodes in people living with dementia: a proof-of-concept studyResearch in context by Marirena Bafaloukou, Ann-Kathrin Schalkamp, Nan Fletcher-Lloyd, Alex Capstick, Chloe Walsh, Cynthia Sandor, Samaneh Kouchaki, Ramin Nilforooshan, Payam Barnaghi

“…We incorporated a traffic-light system to stratify agitation probability estimates supporting clinical decision-making, and employed the SHapley Additive exPlanations (SHAP) framework to enhance interpretability. …”
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Understanding ICU Nursing Knowledge, Perceived Barriers, and Facilitators of Sepsis Recognition and Management: A Cross-Sectional Study by Katherine A. Kissel, MN, RN, Karla D. Krewulak, PhD, Thérèse G. Poulin, BSc, Ken Kuljit S. Parhar, MD, MSc, Daniel J. Niven, MD, PhD, Vanessa M. Doiron, MScHQ, RN, Kirsten M. Fiest, PhD

“…Fewer nurses agreed it was easy to identify priority sepsis interventions based on order urgency (n = 53, 53%, TDF domain Memory, Attention, and Decision Processes). …”
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Enhancing Orthopedic Surgery and Treatment Using Artificial Intelligence and Its Application in Health and Dietary Welfare by D. Rubidha Devi, S. K. UmaMaheswaran, Sandhya Tarar, Abu Sarwar Zamani, Durgaprasad Gangodkar, A. Prabhu Chakkaravarthy, IssahAbubakari Samori

“…The current decade has seen an increased usage of high-end digital technologies like machine learning in the field of health care services which enable in supporting and performing different functions with less or no human interventions. …”
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Balancing co-benefits and trade-offs between climate change mitigation and adaptation innovations under mixed crop-livestock systems in semi-arid Zimbabwe by Sabine Homann-Kee Tui, Roberto O. Valdivia, Katrien Descheemaeker, Gevious Sisito, Elisha N. Moyo, Farai Mapanda

“…They include market-oriented adaptation and mitigation interventions and social protection mechanisms that would support the transition towards more sustainable intensified, diversified and better integrated crop-livestock systems. …”
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Alcohol, alcoholism and related consequences by M. V. Mayevskaya

“…Nalmefene, blocking these receptors, reduces supporting effect of alcohol which ceases to deliver expected pleasure.Conclusion. …”
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Real-time geo-decisional system for risk and disaster management in Madagascar by Rakotoarison Tsiorinantenaina René, Hajalalaina Aimé Richard

“…In terms of prevention, Madagascar already has an early warning system to inform the population, but during a crisis, it still lacks a decision support system for rapid, real-time intervention to minimize damage. …”
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Informing climate-health adaptation options through mapping the needs and potential for integrated climate-driven early warning forecasting systems in South Asia-A scoping review. by Festus A Asaaga, Emmanuel S Tomude, Nathan J Rickards, Richard Hassall, Sunita Sarkar, Bethan V Purse

“…<h4>Results</h4>We found very few robust, evidenced-based forecasting systems for climate- and water- sensitive infectious diseases, which suggests limited operationalisation of decision-support tools that could inform actions to reduce disease burdens in the region. …”
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Real-Time Deployment of Ultrasound Image Interpretation AI Models for Emergency Medicine Triage Using a Swine Model by Sofia I. Hernandez Torres, Lawrence Holland, Theodore Winter, Ryan Ortiz, Krysta-Lynn Amezcua, Austin Ruiz, Catherine R. Thorpe, Eric J. Snider

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Unplanned Student Pregnancy: Should the University Be There for Its Students? by E. Yu. Samuticheva

“…On the basis of the received answers the author suggests changing the policy of the universities by increasing their role in discouraging students from the termination decision and provide recommendations for the creation of the anti-crisis team, offering the support to the students in difficult life situations including cases of the unplanned pregnancy. …”
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Posterior Positivity Distribution Analysis of Subclinical Bluetongue in the Eastern and North-Eastern States of India: A Wakeup Call for Outbreak Preparedness by Siddhartha Narayan Joardar, Aritra Sanyal, Ahmed Abd El Wahed, Saibal Ray

“…The MCMC method was applied for the first time in a BTV seroprevalence analysis that enhanced our understanding of infection dynamics, guided targeted interventions and supported better decision-making in bluetongue disease control, prevention and disease preparedness. …”
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Good-Quality mHealth Apps for Endometriosis Care: Systematic Search by Diksha Sirohi, Cecilia HM Ng, Niranjan Bidargaddi, Helen Slater, Melissa Parker, M Louise Hull, Rebecca O'Hara

“…In addition, clinicians use mHealth apps for continued medical education and clinical decision-making and recommend good-quality apps to patients. …”
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Identification of multiple complications as independent risk factors associated with 1-, 3-, and 5-year mortality in hepatitis B-associated cirrhosis patients by Duo Shen, Ling Sha, Ling Yang, Xuefeng Gu

“…Eight machine learning techniques were employed to construct predictive models, including C5.0, linear discriminant analysis (LDA), least absolute shrinkage and selection operator (LASSO), k-nearest neighbour (KNN), gradient boosting decision tree (GBDT), support vector machine (SVM), generalised linear model (GLM) and naive Bayes (NB), utilising variables such as medical history, demographics, clinical signs, and laboratory test results. …”
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Protocol paper: randomized controlled trial of the smart online-to-offline model development for chronic diseases management through digital health in real world setting by Jae Eun Shin, Juho Choi, Heejung Lee, Suk-Won Lee, Juhwan Oh

“…Participants are randomized into treatment and control arms for each group. The intervention includes personalized digital healthcare support, continuous data monitoring, and online education, with primary care provided by healthcare professionals. …”
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