AIMC Topic: Middle Aged

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Classification of Current Procedural Terminology Codes from Electronic Health Record Data Using Machine Learning.

Anesthesiology
BACKGROUND: Accurate anesthesiology procedure code data are essential to quality improvement, research, and reimbursement tasks within anesthesiology practices. Advanced data science techniques, including machine learning and natural language process...

Role of Instruction Adherence During Highly Structured Robotic Arm Training on Motor Outcomes for Individuals After Chronic Stroke.

American journal of physical medicine & rehabilitation
The aim of this study was to examine the effects of instruction adherence on upper limb motor outcomes after highly structured intervention. A secondary data analysis was completed using mixed linear modeling design. Thirty chronic stroke survivors w...

Multiple Plasma Biomarkers for Risk Stratification in Patients With Heart Failure and Preserved Ejection Fraction.

Journal of the American College of Cardiology
BACKGROUND: Better risk stratification strategies are needed to enhance clinical care and trial design in heart failure with preserved ejection fraction (HFpEF).

Semi-automated prediction approach of target shifts using machine learning with anatomical features between planning and pretreatment CT images in prostate radiotherapy.

Journal of radiation research
The goal of this study was to develop a semi-automated prediction approach of target shifts using machine learning architecture (MLA) with anatomical features for prostate radiotherapy. Our hypothesis was that anatomical features between planning com...

A Machine-Learning Approach to Predicting Smoking Cessation Treatment Outcomes.

Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
AIMS: Most cigarette smokers want to quit smoking and more than half make an attempt every year, but less than 10% remain abstinent for at least 6 months. Evidence-based tobacco use treatment improves the likelihood of quitting, but more than two-thi...

A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas.

Neuro-oncology
BACKGROUND: Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. Currently, reliable IDH mutation determination requires invasive surgical procedures. The purpose of this study was to develop a high...

Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain.

Neuro-oncology
BACKGROUND: Conventional MRI cannot be used to identify H3 K27M mutation status. This study aimed to investigate the feasibility of predicting H3 K27M mutation status by applying an automated machine learning (autoML) approach to the MR radiomics fea...

Machine learning reveals serum sphingolipids as cholesterol-independent biomarkers of coronary artery disease.

The Journal of clinical investigation
BACKGROUNDCeramides are sphingolipids that play causative roles in diabetes and heart disease, with their serum levels measured clinically as biomarkers of cardiovascular disease (CVD).METHODSWe performed targeted lipidomics on serum samples from ind...

Application of Convolutional Neural Networks for Detection of Superficial Nonampullary Duodenal Epithelial Tumors in Esophagogastroduodenoscopic Images.

Clinical and translational gastroenterology
OBJECTIVES: A superficial nonampullary duodenal epithelial tumor (SNADET) is defined as a mucosal or submucosal sporadic tumor of the duodenum that does not arise from the papilla of Vater. SNADETs rarely metastasize to the lymph nodes, and most can ...