AIMC Topic: Risk Factors

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Early gestational diabetes mellitus risk predictor using neural network with NearMiss.

Gynecological endocrinology : the official journal of the International Society of Gynecological Endocrinology
BACKGROUND: Gestational diabetes mellitus (GDM) is globally recognized as a significant pregnancy-related condition, contributing to complex complications for both mothers and infants. Traditional glucose tolerance tests lack the ability to identify ...

Construction and validation of a predictive model for meningoencephalitis in pediatric scrub typhus based on machine learning algorithms.

Emerging microbes & infections
To retrospectively analyze the clinical characteristics of pediatric scrub typhus (ST) with meningoencephalitis (STME) and to construct and validate predictive models using machine learning.Clinical data were collected from 100 cases of pediatric STM...

Machine learning-based analyses of contributing factors for the development of hypertension: a comparative study.

Clinical and experimental hypertension (New York, N.Y. : 1993)
OBJECTIVES: Sufficient attention has not been given to machine learning (ML) models using longitudinal data for investigating important predictors of new onset of hypertension. We investigated the predictive ability of several ML models for the devel...

AI in Hypertensive Disorders of Pregnancy: Review.

American journal of hypertension
BACKGROUND: Hypertensive disorders of pregnancy (HDP) are a leading cause of maternal and fetal mortality worldwide. Early detection and risk stratification are critical for timely intervention to prevent severe complications such as eclampsia, strok...

Prevention, Detection, and Management of Post-Endoscopic Retrograde Cholangiopancreatography Pancreatitis.

Gut and liver
Endoscopic retrograde cholangiopancreatography (ERCP) is a widely used diagnostic and therapeutic procedure for pancreaticobiliary diseases. However, its relatively invasive nature necessitates a thorough understanding of potential adverse events and...

Machine learning identifies prominent risk factors for depressive symptoms among Chinese children and adolescents.

Journal of affective disorders
BACKGROUND: Identifying key risk factors for depressive symptoms in children and adolescents is crucial for prevention. However, few studies have explored this topic. This study aimed to examine the prevalence of depressive symptoms in Chinese childr...

Association and prediction of serum lipid profiles with incident stroke in the CHARLS cohort: A machine learning analysis.

Medicine
Using the 2011 baseline data of the China health and retirement longitudinal study, we examined the associations between serum lipids and other risk factors and incident stroke, and developed and compared multiple machine learning models for stroke-r...