Postpartum depression (PPD), a common mental illness among mothers, can affect the well-being of both mothers and their children. Early intervention is essential but hindered by difficulties in identifying at-risk women, as it remains unclear how soo...
Depression among older adults is a critical public health issue, particularly when coexisting with non-communicable diseases (NCDs). In India, where population ageing and NCDs burden are rising rapidly, scalable data-driven approaches are needed to i...
OBJECTIVE: Cardiometabolic multimorbidity (CMM) significantly contributes to the economic burden in China, particularly in rural areas. This study aimed to analyze the spatiotemporal distribution of CMM and identify its primary influencing factors in...
BACKGROUND: Early mortality prediction in critically ill patients with cardiovascular disease remains challenging. This study aimed to develop and validate an ensemble machine learning (ML) model to predict 30-day mortality, comparing its performance...
This study presents a novel deep learning (DL) framework, the Deep Neural Frailty Competing Risks (DNFCR) model, which simultaneously integrates frailty and competing risks (CR) for mortality prediction in heart failure (HF). While existing models li...
BACKGROUND: Hookworm infection, a neglected tropical disease (NTD) causing iron-deficiency anaemia and malnutrition in low-income populations with poor sanitation, poses a considerable public health challenge in China and worldwide.
AIM: To identify subgroups of early rheumatoid arthritis (RA) based on comorbidities and RA manifestations and to investigate their associated risks of cardiovascular events and mortality.
OBJECTIVE: To develop a predictive framework integrating machine learning and clinical parameters for postoperative pulmonary complications (PPCs) in non-small cell lung cancer (NSCLC) patients undergoing video-assisted thoracic surgery (VATS).
Patients with type 2 diabetes mellitus (T2DM) have a significantly higher risk of cardiovascular disease (CVD) compared to the general population. Accurately predicting this risk is crucial for developing personalized treatment plans and public healt...
OBJECTIVE: The geriatric nutritional risk index (GNRI) is a promising tool for predicting nutrition-related complications in older adults. This study aimed to explore the association between GNRI and asthma in individuals aged 60 and above.
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