AIMC Topic: Risk Factors

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Association between the (neutrophil + monocyte)/albumin ratio and all-cause mortality in sepsis patients: a retrospective cohort study and predictive model establishment according to machine learning.

BMC infectious diseases
INTRODUCTION: Sepsis is a life-threatening condition characterized by widespread inflammatory response syndrome in the body resulting from infection. Previous studies have demonstrated that some inflammatory factors or nutritional elements contribute...

Machine learning based identification of suicidal ideation using non-suicidal predictors in a university mental health clinic.

Scientific reports
Suicide causes over 700,000 deaths annually worldwide. Mental disorders are closely linked to suicidal ideation, but predicting suicide remains complex due to the multifaceted nature of contributing factors. Traditional assessment tools often fail to...

Initial seizure episodes risk factors identification during hospitalization of ICU patients: A retrospective analysis of the eICU collaborative research database.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
BACKGROUND: We aimed to identify risk factors for initial seizure episodes in ICU patients using various machine learning algorithms.

A machine learning model for predicting acute respiratory distress syndrome risk in patients with sepsis using circulating immune cell parameters: a retrospective study.

BMC infectious diseases
BACKGROUND: Acute respiratory distress syndrome (ARDS) is a severe complication associated with a high mortality rate in patients with sepsis. Early identification of patients with sepsis at high risk of developing ARDS is crucial for timely interven...

Alzheimer's Disease Dementia Prevalence in the United States: A County-Level Spatial Machine Learning Analysis.

American journal of Alzheimer's disease and other dementias
A growing body of literature has examined the impact of neighborhood characteristics on Alzheimer's disease (AD) dementia, yet the spatial variability and relative importance of the most influential factors remain underexplored. We compiled various w...

A Deep Learning Survival Model for Evaluating the Survival Prognosis of Papillary Thyroid Cancer: A Population-Based Cohort Study.

Annals of surgical oncology
BACKGROUND: Deep learning can assess the individual survival prognosis in sizeable datasets with intricate underlying processes. However, studies exploring the performance of deep learning survival in papillary thyroid cancer (PTC) are lacking. This ...

Artificial intelligence to predict cancer risk, are we there yet? A comprehensive review across cancer types.

European journal of cancer (Oxford, England : 1990)
Cancer remains the second leading cause of death worldwide, representing a substantial challenge to global health. Although traditional risk prediction models have played a crucial role in epidemiology of several cancer types, they have limitations e...

Development and external validation of a machine learning model to predict bronchopulmonary dysplasia using dynamic factors.

Scientific reports
We hypothesized that incorporating postnatal dynamic factors would enhance the prediction accuracy of bronchopulmonary dysplasia in preterm infants. This retrospective cohort study included neonates born before 32 weeks of gestation at Seoul National...

Identification and validation of a novel machine learning model for predicting severe pelvic endometriosis: A retrospective study.

Scientific reports
This study aimed to explore potential risk factors for severe endometriosis and to develop a model to predict the risk of severe endometriosis. A total of 308 patients with endometriosis were analyzed. Least absolute shrinkage and selection operator ...