AIMC Topic: Risk Assessment

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Development and evaluation of a machine learning-based point-of-care screening tool for genetic syndromes in children: a multinational retrospective study.

The Lancet. Digital health
BACKGROUND: Delays in the diagnosis of genetic syndromes are common, particularly in low and middle-income countries with limited access to genetic screening services. We, therefore, aimed to develop and evaluate a machine learning-based screening te...

A machine-learning-based method to predict adverse events in patients with dilated cardiomyopathy and severely reduced ejection fractions.

The British journal of radiology
OBJECTIVE: Patients with dilated cardiomyopathy (DCM) and severely reduced left ventricular ejection fractions (LVEFs) are at very high risks of experiencing adverse cardiac events. A machine learning (ML) method could enable more effective risk stra...

Identification of Autistic Risk Candidate Genes and Toxic Chemicals via Multilabel Learning.

IEEE transactions on neural networks and learning systems
As a group of complex neurodevelopmental disorders, autism spectrum disorder (ASD) has been reported to have a high overall prevalence, showing an unprecedented spurt since 2000. Due to the unclear pathomechanism of ASD, it is challenging to diagnose...

Machine learning models of ischemia/hemorrhage in moyamoya disease and analysis of its risk factors.

Clinical neurology and neurosurgery
OBJECT: This study aimed to determine the risk factors of ischemic/hemorrhagic stroke in patients suffering moyamoya disease (MMD), as well as to compare the effects of six analysis methods.

Risk prediction of diabetic nephropathy using machine learning techniques: A pilot study with secondary data.

Diabetes & metabolic syndrome
AIMS: This research work presented a comparative study of machine learning (ML), including two objectives: (i) determination of the risk factors of diabetic nephropathy (DN) based on principal component analysis (PCA) via different cutoffs; (ii) pred...

Machine Learning to Predict Fascial Dehiscence after Exploratory Laparotomy Surgery.

The Journal of surgical research
BACKGROUND: Fascial dehiscence following exploratory laparotomy is associated with significant morbidity and increased mortality. Previously published risk prediction models for fascial dehiscence are dated and limit a surgeon's ability to perform re...

Accelerating the pace of ecotoxicological assessment using artificial intelligence.

Ambio
Species Sensitivity Distribution (SSD) is a key metric for understanding the potential ecotoxicological impacts of chemicals. However, SSDs have been developed to estimate for only handful of chemicals due to the scarcity of experimental toxicity dat...

Fully Automated Deep Learning Tool for Sarcopenia Assessment on CT: L1 Versus L3 Vertebral Level Muscle Measurements for Opportunistic Prediction of Adverse Clinical Outcomes.

AJR. American journal of roentgenology
Sarcopenia is associated with adverse clinical outcomes. CT-based skeletal muscle measurements for sarcopenia assessment are most commonly performed at the L3 vertebral level. The purpose of this article is to compare the utility of fully automated...