ADHD/ADD

Analyzing an organism's sensors using Maximum Entropy (MaxEnt) models with bias, variance, and confusion matrices.

Biological organisms have sensors that communicate information about the environment. Analyzing how well these biological sensors function has usually been done with mutual information between the sensor signal and the environment, but that can be computationally intractable and summarize something quite complex with just a single number. We suggest that alternatively, one may profitably analyze t...

The diagnostic value of the circadian rhythm gene KLF10 in anxiety-depressive disorders and its neuroimmune regulatory mechanisms.

BACKGROUND: Anxiety-depressive disorder (ADD) is one of the important subtypes of depression, which has been shown to be closely related to neuroimmune abnormalities. Currently, specific biomarkers have not been identified as diagnostic, differential, and therapeutic targets. This study aimed to explore the molecular mechanisms and diagnostic biomarkers of ADD and elucidate its association with ne...

Large language models in respiratory care: safety and clinical integration.

BACKGROUND: Large language models (LLMs) are rapidly entering respiratory medicine workflows. Their clinical role remains unclear. A central concern i...

Machine Learning Prediction of First Suicide Attempts in Early Adolescence Among Child Ideators.

OBJECTIVE: Identifying those at highest risk of making a first suicide attempt during adolescence is crucial to inform early suicide prevention. Our s...

Physician oath, followership and leadership.

Physician oaths are often treated as ceremonial relics or ethical ornaments of graduation. I have come to think they are something more demanding: ear...

Adjunctive utility of gaze-based indices obtained with Gazefinder for ADHD assessment in children.

Diagnosis of attention-deficit/hyperactivity disorder (ADHD) relies primarily on subjective clinical assessments, necessitating the establishment of o...

Smartphone-based optical and spectroscopic sensors for food safety monitoring: principles, applications, and future directions.

Food safety monitoring increasingly requires analytical tools that are rapid, portable, and accessible beyond centralized laboratories. In this contex...

Establishing diagnostic thresholds for adult ADHD screening in taxi drivers: validation of the CAARS-S via Item Response Theory and Machine Learning.

BACKGROUND: Adult Attention-Deficit/Hyperactivity Disorder (ADHD) is under-recognized in professional drivers, yet it poses significant safety risks. ...

From machine learning to deep learning in attention deficit hyperactivity disorder diagnosis: A bibliometric analysis of global trends (2011-2024).

BACKGROUND: The diagnosis of attention deficit hyperactivity disorder (ADHD) has traditionally relied on subjective clinical interviews. Recent years ...

Conformal reliability assessment of PPG-based ICU arrhythmia classification under class imbalance and patient heterogeneity.

Deep learning models for photoplethysmography (PPG)-based arrhythmia detection in intensive care are often evaluated by average accuracy, while confor...

Balancing technical and social controls across performance measurement and management stages: a bibliometric synthesis of digitally enabled healthcare performance management.

PURPOSE: An integrated, field-level synthesis of digitally enabled performance measurement and management systems (PM/PMS) in healthcare is provided, ...

Subthalamic spatio-spectral-connectivity of psychiatric symptoms in Parkinson's disease.

Psychiatric symptoms in Parkinson's disease (PD) are highly prevalent and challenging to treat. This study maps oscillatory neural activity to diverse...

Health databases and early identification of autism spectrum disorder: a scoping review.

OBJECTIVES: To map the available evidence on the use of health databases for the early identification of autism spectrum disorder (ASD) across diverse...

Inflammation-linked aging signals in frozen single-cell foundation models: donor-aware detection and robustness testing.

Single-cell foundation models such as scGPT and Geneformer are large neural networks trained on human single-cell RNA-seq data. They were never shown ...

Transdiagnostic monoamine-based subtyping for attention-deficit/hyperactivity disorder and autism spectrum disorder via unsupervised machine learning.

Neuroimaging and molecular studies have examined the etiology of attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). H...

Machine learning reveals a multimodal, transdiagnostic signature of emotion dysregulation vulnerability across patients, offspring, and controls.

Emotion dysregulation (ED) is a core transdiagnostic feature of several psychiatric disorders, including borderline personality disorder, bipolar diso...

Machine Learning Prediction of Hoehn and Yahr Scores at 5-Years Post-123I-Ioflupane SPECT Imaging in a Real-World Parkinson's Disease Dataset.

BACKGROUND: Parkinson's disease (PD) progression is highly heterogeneous, complicating clinical management and prognostication. While machine learning...

The effectiveness of adding on or switching antiseizure medications after the first fails to control focal epilepsy: A systematic review of randomized controlled trials.

Focal epilepsy constitutes 60-70% of epilepsy, and up to half of patients do not achieve seizure freedom with their first antiseizure medication (ASM)...

Intracardiac Echocardiography-Guided Left Atrial Appendage Occlusion: A Comprehensive Narrative Review.

Intracardiac echocardiography (ICE) is an alternative to transesophageal echocardiography for imaging guidance during left atrial appendage occlusion ...

A Framework for Predicting Neurofeedback Treatment Response in ADHD Using EEG Functional Connectivity and Genetic Algorithm-Driven Channel Selection.

In this article, we present a computational framework for predicting treatment response to neurofeedback (NF) among patients with Attention-Deficit/Hy...