AIMC Topic: Risk Factors

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Temporal trends and machine learning prediction of depressive symptoms among Chinese middle-aged and elderly individuals: a national cohort study.

BMC public health
BACKGROUND: The prevalence of depression symptoms, the third most disabling disease worldwide, is as high as 11.5%-21.1% in China's middle-aged and elderly population and increases significantly with age. It is crucial to identify high-risk groups ef...

Development of a machine learning-based depression risk identification tool for older adults with asthma.

BMC psychiatry
BACKGROUND: Asthma is a chronic inflammatory disorder that adversely affects the quality of life, particularly in older adults. The coexistence of depression in asthma patients complicates their management and exacerbates health outcomes. This study ...

A machine learning approach to identify patients at risk for long-term consequences after pulmonary embolism.

Scientific reports
Pulmonary embolism (PE) can result in long-term sequelae, such as post-PE syndrome, including persistent dyspnea and chronic thromboembolic pulmonary hypertension (CTEPH). Existing prediction tools for severe post-PE complications lack sensitivity an...

Interpretable Machine Learning Model for Pulmonary Hypertension Risk Prediction: Retrospective Cohort Study.

JMIR medical informatics
BACKGROUND: Pulmonary hypertension (PH) is a progressive disorder characterized by elevated pulmonary artery pressure and increased pulmonary vascular resistance, ultimately leading to right heart failure. Early detection is critical for improving pa...

Letter to the editor: interpretable machine learning model predicts 1‑year inguinal hernia risk after robot‑assisted radical prostatectomy.

Journal of robotic surgery
We read with interest the recent article by Yu et al., "Interpretable machine learning model predicts 1-year inguinal hernia risk after robot-assisted radical prostatectomy" (DOI: 10.1007/s11701-025-02723-5) , which represents an important step in ap...

Risk prediction of all-cause mortality in hospitalized patients with severe acute pancreatitis by serum urea nitrogen/albumin ratio.

PloS one
BACKGROUND: Classification of risk levels in patients with acute pancreatitis remains a difficult task. Although some biomarkers have emerged to predict the prognosis of patients with acute pancreatitis, they have not been widely used in clinical pra...

Identifying determinants of readmission and death post-stroke using explainable machine learning.

PloS one
BACKGROUND: Stroke remains a global health challenge with high rates of mortality and rehospitalization placing significant demands on healthcare systems. Identifying factors that determine outcomes of post-hospitalization improves resource allocatio...

Development of a prostate cancer biochemical recurrence risk signature using machine learning and motor protein-related genes.

PloS one
BACKGROUND: Motor proteins play significant roles in cancer progression, but their involvement in biochemical recurrence (BCR) of prostate cancer remains unclear. The objective of the study is to develop a prognostic indicator for BCR using machine l...

Machine learning-based evaluation of risk factors for carbapenem-resistant dissemination in neonatal units.

mSystems
Healthcare-associated infections (HAIs), particularly in neonatal intensive care units (NICUs), pose significant challenges due to neonates' vulnerability and the rapid infection spread. However, risk factors facilitating pathogen persistence and dis...