AIMC Topic: Middle Aged

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Development of an equation to predict delta bilirubin levels using machine learning.

Clinica chimica acta; international journal of clinical chemistry
OBJECTIVE: Delta bilirubin (albumin-covalently bound bilirubin) may provide important clinical utility in identifying impaired hepatic excretion of conjugated bilirubin, but it cannot be measured in real-time for diagnostic purposes in clinical labor...

Machine learning models to predict osteonecrosis in patients with femoral neck fractures undergoing internal fixation.

Injury
OBJECTIVE: This study aimed to use machine learning (ML) to establish risk factor and prediction models of osteonecrosis of the femoral head (ONFH) in patients with femoral neck fractures (FNFs) after internal fixation.

Incidental pulmonary nodules: Natural language processing analysis of radiology reports.

Respiratory medicine and research
BACKGROUND: Pulmonary nodules are a common incidental finding on chest Computed Tomography scans (CT), most of the time outside of lung cancer screening (LCS). We aimed to evaluate the number of incidental pulmonary nodules (IPN) found in 1 year in o...

Investigating the role of artificial intelligence in predicting perceived dysphonia level.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
PURPOSE: This study aims to investigate the role of one of these models in the field of voice pathology and compare its performance in distinguishing the perceived dysphonia level.

Multiparametric Ultrasound Imaging of Prostate Cancer Using Deep Neural Networks.

Ultrasound in medicine & biology
OBJECTIVE: A deep neural network (DNN) was trained to generate a multiparametric ultrasound (mpUS) volume from four input ultrasound-based modalities (acoustic radiation force impulse [ARFI] imaging, shear wave elasticity imaging [SWEI], quantitative...

Interpretable machine learning models based on shear-wave elastography radiomics for predicting cardiovascular disease in diabetic kidney disease patients.

Journal of diabetes investigation
BACKGROUND: The risk of cardiovascular complications is significantly elevated in patients with diabetic kidney disease (DKD). Recognizing the link between the progression of DKD and an increased risk of cardiovascular disease (CVD), it is crucial to...

Machine learning models for temporally precise lapse prediction in alcohol use disorder.

Journal of psychopathology and clinical science
We developed three machine learning models that predict hour-by-hour probabilities of a future lapse back to alcohol use with increasing temporal precision (i.e., lapses in the next week, next day, and next hour). Model features were based on raw sco...

Investigation of emergency department abandonment rates using machine learning algorithms in a single centre study.

Scientific reports
A critical problem that Emergency Departments (EDs) must address is overcrowding, as it causes extended waiting times and increased patient dissatisfaction, both of which are immediately linked to a greater number of patients who leave the ED early, ...

Recruitment in Appalachian, Rural and Older Adult Populations in an Artificial Intelligence World: Study Using Human-Mediated Follow-Up.

JMIR formative research
BACKGROUND: Participant recruitment in rural and hard-to-reach (HTR) populations can present unique challenges. These challenges are further exacerbated by the need for low-cost recruiting, which often leads to use of web-based recruitment methods (e...

Robotic assessment of bilateral and unilateral upper limb functions in adults with cerebral palsy.

Journal of neuroengineering and rehabilitation
BACKGROUND: Children with unilateral cerebral palsy (CP) exhibit motor impairments predominantly on one side of the body, while also having ipsilesional and bilateral impairments. These impairments are known to persist through adulthood, but their ex...