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Prevalence, associated factors, and machine learning-based prediction of probable depression among individuals with chronic diseases in Bangladesh | BMC Psychiatry

Prevalence, associated factors, and machine learning-based prediction of probable depression among individuals with chronic diseases in Bangladesh | BMC Psychiatry

admin November 23, 2025

World Health Organization (WHO). Noncommunicable diseases. 23 Dec 2024 . Available: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseasesWorld Health Organization (WHO). The top 10 causes of...

Read MoreRead more about Prevalence, associated factors, and machine learning-based prediction of probable depression among individuals with chronic diseases in Bangladesh | BMC Psychiatry
Integrating Outcome-Based Education (OBE) and Problem-Based Learning (PBL) in psychiatric clinical practice teaching: an empirical study | BMC Medical Education

Integrating Outcome-Based Education (OBE) and Problem-Based Learning (PBL) in psychiatric clinical practice teaching: an empirical study | BMC Medical Education

admin November 19, 2025

Study subjectsOverall, a total of 43 fifth-year undergraduate students majoring in psychiatry who completed their internships at the Huai’an Clinical...

Read MoreRead more about Integrating Outcome-Based Education (OBE) and Problem-Based Learning (PBL) in psychiatric clinical practice teaching: an empirical study | BMC Medical Education
Assessment of a Grad-CAM interpretable deep learning model for HAPE diagnosis: performance and pitfalls in severity stratification from chest radiographs | BMC Medical Informatics and Decision Making

Assessment of a Grad-CAM interpretable deep learning model for HAPE diagnosis: performance and pitfalls in severity stratification from chest radiographs | BMC Medical Informatics and Decision Making

admin November 3, 2025

HAPE is a type of altitude-related noncardiogenic pulmonary edema caused primarily by acute exposure to high altitudes, which leads to...

Read MoreRead more about Assessment of a Grad-CAM interpretable deep learning model for HAPE diagnosis: performance and pitfalls in severity stratification from chest radiographs | BMC Medical Informatics and Decision Making

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