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A prospectively deployed deep learning-enabled automated quality assurance tool for oncological palliative spine radiation therapy.

The Lancet. Digital health
BACKGROUND: Palliative spine radiation therapy is prone to treatment at the wrong anatomic level. We developed a fully automated deep learning-based spine-targeting quality assurance system (DL-SpiQA) for detecting treatment at the wrong anatomic lev...

A Machine Learning Model to Harmonize Volumetric Brain MRI Data for Quantitative Neuroradiologic Assessment of Alzheimer Disease.

Radiology. Artificial intelligence
Purpose To extend a previously developed machine learning algorithm for harmonizing brain volumetric data of individuals undergoing neuroradiologic assessment of Alzheimer disease not encountered during model training. Materials and Methods Neuroharm...

Dissecting the Predictors of Cyber-Aggression Through an Explainable Machine Learning Model.

Aggressive behavior
The general aggression model (GAM) suggests that cyber-aggression stems from individual characteristics and situational contexts. Previous studies have focused on limited factors using linear models, leading to oversimplified predictions. This study ...

Natural Language Processing to Identify Infants Aged 90 Days and Younger With Fevers Prior to Presentation.

Hospital pediatrics
OBJECTIVE: Natural language processing (NLP) can enhance research studies for febrile infants by more comprehensive cohort identification. We aimed to refine and validate an NLP algorithm to identify and extract quantified temperature measurements fr...

PROGNOSTIC ACCURACY OF MACHINE LEARNING MODELS FOR IN-HOSPITAL MORTALITY AMONG CHILDREN WITH PHOENIX SEPSIS ADMITTED TO THE PEDIATRIC INTENSIVE CARE UNIT.

Shock (Augusta, Ga.)
Objective: The Phoenix sepsis criteria define sepsis in children with suspected or confirmed infection who have ≥2 in the Phoenix Sepsis Score. The adoption of the Phoenix sepsis criteria eliminated the Systemic Inflammatory Response Syndrome criteri...

A Dynamic Model for Early Prediction of Alzheimer's Disease by Leveraging Graph Convolutional Networks and Tensor Algebra.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Alzheimer's disease (AD) is a neurocognitive disorder that deteriorates memory and impairs cognitive functions. Mild Cognitive Impairment (MCI) is generally considered as an intermediate phase between normal cognitive aging and more severe conditions...

Uncovering Important Diagnostic Features for Alzheimer's, Parkinson's and Other Dementias Using Interpretable Association Mining Methods.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Alzheimer's Disease and Related Dementias (ADRD) afflict almost 7 million people in the USA alone. The majority of research in ADRD is conducted using post-mortem samples of brain tissue or carefully recruited clinical trial patients. While these res...

Unsupervised Dimensionality Reduction Techniques for the Assessment of ASD Biomarkers.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Autism Spectrum Disorder (ASD) encompasses a range of developmental disabilities marked by differences in social functioning, cognition, and behavior. Both genetic and environmental factors are known to contribute to ASD, yet the exact etiological fa...

Investigating the Differential Impact of Psychosocial Factors by Patient Characteristics and Demographics on Veteran Suicide Risk Through Machine Learning Extraction of Cross-Modal Interactions.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Accurate prediction of suicide risk is crucial for identifying patients with elevated risk burden, helping ensure these patients receive targeted care. The US Department of Veteran Affairs' suicide prediction model primarily leverages structured elec...

Evaluation of temporomandibular joint disc displacement with MRI-based radiomics analysis.

Dento maxillo facial radiology
OBJECTIVES: The purpose of this study was to propose a machine learning model and assess its ability to classify temporomandibular joint (TMJ) disc displacements on MR T1-weighted and proton density-weighted images.