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| http://dx.doi.org/10.1186/cc9999 | DOI Listing |
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Deep Learning-Based Electrocardiogram Model (EIANet) to Predict Emergency Department Cardiac Arrest: Development and External Validation Study.
J Med Internet Res
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Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
Background: In-hospital cardiac arrest (IHCA) is a severe and sudden medical emergency that is characterized by the abrupt cessation of circulatory function, leading to death or irreversible organ damage if not addressed immediately. Emergency department (ED)-based IHCA (EDCA) accounts for 10% to 20% of all IHCA cases. Early detection of EDCA is crucial, yet identifying subtle signs of cardiac deterioration is challenging.
View Article and Find Full Text PDFBreaking the cycle: a systematic review of neurobiological mechanisms and psychotherapeutic innovations in ketamine addiction.
J Addict Dis
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Head of Therapy & Programme, Delamere Health, Cheshire.
This systematic review synthesizes current evidence on non-prescribed ketamine use, emphasizing its neurobiological impacts and psychotherapeutic interventions. Patterns of misuse demonstrate the complex interplay of neurobiological, socio-economic, demographic and psychological factors with adolescents, women and polysubstance users identified as high-risk groups. Neurobiological findings highlight prefrontal-limbic dysconnectivity, maladaptive neuroplasticity alongside hypothalamic-pituitary-adrenal (HPA) axis dysregulation as central mechanisms underlying this addiction.
View Article and Find Full Text PDFA Pilot Study on the Feasibility and Integration of High-Risk Human Papillomavirus (HPV) Testing for Cervical Cancer Prevention in Trinidad and Tobago.
Cureus
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Obstetrics and Gynaecology, San Fernando General Hospital, San Fernando, TTO.
Background Globally, cervical cancer is one of the leading causes of cancer-related death among women. Persistent infection with high-risk human papillomavirus (hrHPV) is the predominant etiological factor for cervical cancer, underscoring the critical need for effective screening to enable early detection and reduce disease burden. However, in Trinidad and Tobago, liquid-based cytology is not available in the public healthcare system, and current screening efforts rely solely on conventional Pap smears.
View Article and Find Full Text PDFPredictive model for daily risk alerts in sepsis patients in the ICU: visualization and clinical analysis of risk indicators.
Precis Clin Med
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Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China.
This study introduces a novel Transformer-based time-series framework designed to revolutionize risk stratification in Intensive Care Units (ICUs) by predicting patient outcomes with high temporal precision. Leveraging sequential data from the eICU database, our two-stage architecture dynamically captures evolving health trajectories throughout a patient's ICU stay, enabling real-time identification of high-risk individuals and actionable insights for personalized interventions. The model demonstrated exceptional predictive power, achieving a progressive AUC increase from 0.
View Article and Find Full Text PDFAssessment of the Sensitivity of a Smartphone App to Assist Patients in the Identification of Stroke and Myocardial Infarction: Cross-Sectional Study.
JMIR Form Res
March 2025
Division of Cardiology, Department of Medicine, Unversity of Saskatchewan, Saskatoon, SK, Canada.
Background: Most people do not recognize symptoms of neurological and cardiac emergencies in a timely manner. This leads to delays in hospital arrival and reduced access to therapies that can open arteries. We created a smartphone app to help patients and families evaluate if symptoms may be high risk for stroke or heart attack (myocardial infarction, MI).
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