AIMC Topic: Predictive Value of Tests

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Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD.

Scientific reports
To identify newborns at risk of developing ASD and to detect ASD biomarkers early after birth, we compared retrospectively ultrasound and biological measurements of babies diagnosed later with ASD or neurotypical (NT) that are collected routinely dur...

Artificial intelligence in cardiovascular CT: Current status and future implications.

Journal of cardiovascular computed tomography
Artificial intelligence (AI) refers to the use of computational techniques to mimic human thought processes and learning capacity. The past decade has seen a rapid proliferation of AI developments for cardiovascular computed tomography (CT). These al...

Research on the predictive effect of a combined model of ARIMA and neural networks on human brucellosis in Shanxi Province, China: a time series predictive analysis.

BMC infectious diseases
BACKGROUND: Brucellosis is a major public health problem that seriously affects developing countries and could cause significant economic losses to the livestock industry and great harm to human health. Reasonable prediction of the incidence is of gr...

Predicting Readmission After Anterior, Posterior, and Posterior Interbody Lumbar Spinal Fusion: A Neural Network Machine Learning Approach.

World neurosurgery
BACKGROUND: Readmission after spine surgery is costly and a relatively common occurrence. Previous research identified several risk factors for readmission; however, the conclusions remain equivocal. Machine learning algorithms offer a unique perspec...

Comparison of machine learning methods for prediction of osteoradionecrosis incidence in patients with head and neck cancer.

The British journal of radiology
OBJECTIVES: Mandible osteoradionecrosis (ORN) is one of the most severe toxicities in patients with head and neck cancer (HNC) undergoing radiotherapy (RT). The existing literature focuses on the correlation of mandible ORN and clinical and dosimetri...

Integrating human services and criminal justice data with claims data to predict risk of opioid overdose among Medicaid beneficiaries: A machine-learning approach.

PloS one
Health system data incompletely capture the social risk factors for drug overdose. This study aimed to improve the accuracy of a machine-learning algorithm to predict opioid overdose risk by integrating human services and criminal justice data with h...

Deep Learning to Estimate Biological Age From Chest Radiographs.

JACC. Cardiovascular imaging
OBJECTIVES: The goal of this study was to assess whether a deep learning estimate of age from a chest radiograph image (CXR-Age) can predict longevity beyond chronological age.