AIMC Topic: Risk Factors

Clear Filters Showing 1131 to 1140 of 2857 articles

Implementation of a Machine Learning Approach Evaluating Risk Factors for Complications after Single-Stage Augmentation Mastopexy.

Aesthetic plastic surgery
BACKGROUND: Single-stage mastopexy augmentation is a much-debated intervention due to its complexity and the associated relatively high complication rates. This study aimed to reevaluate the risk factors for these complications using a novel approach...

Development and external validation of a logistic and a penalized logistic model using machine-learning techniques to predict suicide attempts: A multicenter prospective cohort study in Korea.

Journal of psychiatric research
Despite previous efforts to build statistical models for predicting the risk of suicidal behavior using machine-learning analysis, a high-accuracy model can lead to overfitting. Furthermore, internal validation cannot completely address this problem....

Interpretable machine learning predicts postpartum hemorrhage with severe maternal morbidity in a lower-risk laboring obstetric population.

American journal of obstetrics & gynecology MFM
BACKGROUND: Early identification of patients at increased risk for postpartum hemorrhage (PPH) associated with severe maternal morbidity (SMM) is critical for preparation and preventative intervention. However, prediction is challenging in patients w...

Exploring the relationship between heavy metals and diabetic retinopathy: a machine learning modeling approach.

Scientific reports
Diabetic retinopathy (DR) is one of the leading causes of adult blindness in the United States. Although studies applying traditional statistical methods have revealed that heavy metals may be essential environmental risk factors for diabetic retinop...

Examining the Most Important Risk Factors for Predicting Youth Persistent and Distressing Psychotic-Like Experiences.

Biological psychiatry. Cognitive neuroscience and neuroimaging
BACKGROUND: Persistence and distress distinguish more clinically significant psychotic-like experiences (PLEs) from those that are less likely to be associated with impairment and/or need for care. Identifying risk factors that identify clinically re...

Machine Learning of Cardiac Anatomy and the Risk of New-Onset Atrial Fibrillation After TAVR.

JACC. Clinical electrophysiology
BACKGROUND: New-onset atrial fibrillation (NOAF) occurs in 5% to 15% of patients who undergo transfemoral transcatheter aortic valve replacement (TAVR). Cardiac imaging has been underutilized to predict NOAF following TAVR.

First experiences with machine learning predictions of accelerated declining eGFR slope of living kidney donors 3 years after donation.

Journal of nephrology
BACKGROUND: Living kidney donors are screened pre-donation to estimate the risk of end-stage kidney disease (ESKD). We evaluate Machine Learning (ML) to predict the progression of kidney function deterioration over time using the estimated GFR (eGFR)...

Development of machine learning models to predict perioperative blood transfusion in hip surgery.

BMC medical informatics and decision making
BACKGROUND: Allogeneic Blood transfusion is common in hip surgery but is associated with increased morbidity. Accurate prediction of transfusion risk is necessary for minimizing blood product waste and preoperative decision-making. The study aimed to...