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Post-secondary student adoption of mental health labels through their use of social media: a scoping review.

BACKGROUND: Social media is a significant source of information for post-secondary students, who are usually at the age at which many common mental disorders first express themselves. Social media can have a role in the way post-secondary students identify and act on mental health issues. OBJECTIVES: Explore how the use of social media influences post-secondary students' adoption of mental health ...

Deep neural network-based analysis of voice biomarkers for monitoring treatment response in adolescent major depressive disorder.

BACKGROUND: In adolescents, identifying objective biomarkers for treatment response is crucial for the development of effective interventions. Voice-based biomarkers have recently shown potential to capture treatment-related changes in Major Depressive Disorder (MDD). While prior studies have been cross-sectional experiments with single speech sample, this study addresses a critical gap by evaluat...

Development and interpretation of a dual-energy CT-based deep learning radiomics model for predicting new cerebral ischemic lesions after carotid artery stenting: a multicenter study.

OBJECTIVES: Early recognition of individuals at elevated risk for new ipsilateral ischemic lesions (NIILs) after carotid artery stenting (CAS) is vita...

3D-QALAS synthetic MRI with Zero-DeepSub in children: initial experience including post-contrast imaging feasibility.

BACKGROUND: Magnetic resonance imaging (MRI) in children requires multiple sequences, leading to lengthy exams and motion-related challenges. Syntheti...

A two-stage algorithm to detect electrographically focal seizures using a wearable single-channel EEG sensor.

OBJECTIVE: This paper presents a two-stage machine learning model for electrographic seizure detection using wearable single-channel scalp electroence...

Predicting Infant Sleep Patterns From Postpartum Maternal Mental Health Measures: Machine Learning Approach.

BACKGROUND: Postpartum maternal mental health (MMH) symptoms, including depression, anxiety, and childbirth-related post-traumatic stress disorder, ar...

Predictive mixed-gas detection using rGO/In2O3 nanocomposite sensors assisted by machine learning.

Selectivity towards specific analytes and detection at sub-ppm levels remain significant challenges for chemiresistive gas sensors. Hybrid materials, ...

Enhancing upper limb motor recovery prediction after acute stroke using EEG and subacute data.

Electroencephalography (EEG) has shown promise in assessing and monitoring functional recovery in stroke survivors, but its utility in predicting uppe...

Explainable Temporal Inference for Irregular Multivariate Time Series. A Case Study for Early Prediction of Multidrug Resistance.

OBJECTIVE: Many healthcare problems involve complex patient trajectories represented as Multivariate Time Series (MTS), with predictions often coming ...

Graph Convolutional Networks Enable Fast Hemorrhagic Stroke Monitoring With Electrical Impedance Tomography.

OBJECTIVE: To develop a fast image reconstruction method for stroke monitoring with electrical impedance tomography with image quality comparable to c...

Convolutional neural network-derived neurofibrillary tangle classifiers: Investigative tools to identify maturation levels and explore post-translational modifications using laser capture microdissection coupled mass spectrometry.

Post-translational modifications (PTM) of tau are implicated in Alzheimer disease (AD) progression and are established biomarkers in cerebrospinal flu...

An Explainable Machine Learning Model for Predicting Short-Term Haemodynamic Changes Post-TIPS With Prognostic Implications.

BACKGROUND AND AIMS: While remeasuring portacaval pressure gradient (PPG) after transjugular intrahepatic portosystemic shunt (TIPS), it provides supe...

Mitigating Disparities in Prostate Cancer Survival Prediction Through Fairness-Aware Machine Learning Models.

PURPOSE: Prediction models can contribute to disparities in care by performing unequally across demographic groups. While fairness-aware methods have ...

Effects of ABO-Incompatible Blood Transfusion on Immune Response and Rejection After Organ Transplantation.

BACKGROUND: Peri-operative blood transfusion is common during solid-organ transplantation, yet the impact of ABO-incompatible (ABOi) blood exposure on...

Scanner-integrated reconstruction versus post-processing deep learning for low-count 18F-FDG PET/CT: a comparative clinical evaluation.

OBJECTIVES: To compare two deep learning (DL) approaches for low-count PET/CT: deep progressive reconstruction (DPR), a scanner-integrated reconstruct...

An interpretable machine learning framework for adverse drug reaction prediction from drug-target interactions.

BACKGROUND: Adverse drug reactions (ADRs) present challenges to patient safety and healthcare systems. Current pharmacovigilance methods, such as the ...

Organ preservation in rectal cancer: an endoscopist's perspective on current evidence and future directions.

Rectal cancer management has increasingly shifted toward organ-preserving strategies that aim to maintain oncologic control while preserving bowel, ur...

Predicting postoperative delirium after lung cancer resection: the utility of synthetic data and LIME algorithm for model interpretation.

To construct an efficient predictive model for post-lung cancer resection delirium (POD) using artificial intelligence, with a focus on leveraging syn...

Machine learning models for drug-drug interaction prediction from computational discovery to clinical application.

Drug-drug interaction (DDI) poses a major challenge in clinical pharmacology, often compromising therapeutic efficacy or causing serious adverse event...

Development and validation of a neural network survival prediction model for ischemic heart disease.

BACKGROUND: Current risk prediction models for ischemic heart disease in clinical use are relatively simple and use a limited collection of well-known...