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Risk Factors

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Application of machine learning in identifying risk factors for low APGAR scores.

BMC pregnancy and childbirth
BACKGROUND: Identifying the risk factors for low APGAR scores at birth is critical for improving neonatal outcomes and guiding clinical interventions.

Optimizing Strategy for Lung Cancer Screening: From Risk Prediction to Clinical Decision Support.

JCO clinical cancer informatics
PURPOSE: Low-dose computed tomography (LDCT) screening is effective in reducing lung cancer mortality by detecting the disease at earlier, more treatable stages. However, high false-positive rates and the associated risks of subsequent invasive diagn...

Predicting Agitation Events in the Emergency Department Through Artificial Intelligence.

JAMA network open
IMPORTANCE: Agitation events are increasing in emergency departments (EDs), exacerbating safety risks for patients and clinicians. A wide range of clinical etiologies and behavioral patterns in the emergency setting make agitation prediction difficul...

AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.

NAR genomics and bioinformatics
The convergence of artificial intelligence (AI) and biomedical data is transforming precision medicine by enabling the use of genetic risk factors (GRFs) for customized healthcare services based on individual needs. Although GRFs play an essential ro...

Forecasting Subjective Cognitive Decline: AI Approach Using Dynamic Bayesian Networks.

Journal of medical Internet research
BACKGROUND: Several potentially modifiable risk factors are associated with subjective cognitive decline (SCD). However, developmental patterns of these risk factors have not been used before to forecast later SCD. Practical tools for the prevention ...

Machine learning-based predictive modelling of mental health in Rwandan Youth.

Scientific reports
Globally, mental disorders are a significant burden, particularly in low- and middle-income countries, with high prevalence in Rwanda, especially among survivors of the 1994 genocide against Tutsi. Machine learning offers promise in predicting mental...

Identifying most important predictors for suicidal thoughts and behaviours among healthcare workers active during the Spain COVID-19 pandemic: a machine-learning approach.

Epidemiology and psychiatric sciences
AIMS: Studies conducted during the COVID-19 pandemic found high occurrence of suicidal thoughts and behaviours (STBs) among healthcare workers (HCWs). The current study aimed to (1) develop a machine learning-based prediction model for future STBs us...

Predicting peripartum depression using elastic net regression and machine learning: the role of remnant cholesterol.

BMC pregnancy and childbirth
BACKGROUND: Traditional statistical methods have dominated research on peripartum depression (PPD), but innovative approaches may provide deeper insights. This study aims to predict the impact factors of PPD using elastic net regression (ENR) combine...

Identifying individuals at risk of post-stroke depression: Development and validation of a predictive model.

Saudi medical journal
OBJECTIVES: To identify the factors associated with post-stroke depression (PSD) and develop a machine learning predictive model using a large dataset, considering sociodemographic, lifestyle, and clinical factors.

Unveiling new insights into migraine risk stratification using machine learning models of adjustable risk factors.

The journal of headache and pain
BACKGROUND: Migraine ranks as the second-leading cause of global neurological disability, affecting approximately 1.1 billion individuals worldwide with severe quality-of-life impairments. Although adjustable risk factors-including environmental expo...