Prescriptions

Enhanced multimodal MRI classification of schizophrenia through cross-attention graph neural networks.

We propose TB-GCAN, a tri-branch cross-attention graph neural network for schizophrenia classification using multimodal MRI, including sMRI, fMRI, and DTI. Built on a multi-site dataset of 1191 samples from seven scanning sites, the model exploits atlas-defined one-to-one anatomical correspondence across modalities to enable node-level cross-attention during intermediate representation learning. I...

Enhancing virtual screening of cystathionine β-synthase inhibitors: benchmarking target-specific machine-learning scoring functions against state-of-the-art AI docking and co-folding approaches.

Cystathionine β-synthase (CBS) has emerged as an important therapeutic target implicated in cancer and Down syndrome, yet the discovery of selective CBS inhibitors remains challenging due to limited structural diversity of known ligands and the scarcity of target-focused virtual screening (VS) benchmarks. In this study, we present the first comprehensive evaluation of CBS-specific artificial intel...

Comprehensive plant disease classification and severity estimation for sustainable farming via automatic segmentation and multi-scale feature fusion.

Detecting plant leaf diseases at an early stage is one of the most important requirements for sustainable agriculture, increasing crop productivity, a...

Artificial Intelligence Across the Vaccine Clinical Trial Lifecycle: Evidence, Readiness, and Guardrails.

Artificial intelligence (AI) is increasingly being used to support clinical research, but its value in vaccine clinical trials requires careful eviden...

Bias and Fairness Across the Healthcare AI Lifecycle: A Clinician-Oriented Review.

Artificial intelligence (AI) is increasingly being investigated and, in selected clinical settings, implemented to support diagnosis, triage, and work...

Therapeutic hybrid intelligence with neural and knowledge-based expert reasoning for SRS (THINKERS): an AI model for meningioma.

BACKGROUND: Prescription dose selection for intracranial meningiomas treated with stereotactic radiosurgery remains guided by tumor volume, anatomical...

First-episode psychosis and mood disorder onset in individuals with epilepsy.

BACKGROUND: Psychiatric disorders represent a major burden for patients with epilepsy (PwE). This study examined how demographic, epilepsy-related, an...

X-DILiver: an ensemble learning framework for predicting drug-induced liver injury.

Drug-induced liver injury (DILI) is a primary cause of drug attrition, associated with over 1000 medications and accounting for 32% of marketed drugs ...

A quantitative study on user interaction and brand perception of visual elements in social media advertising driven by deep learning.

This paper focuses on the field of sentiment analysis for social media advertisements, specifically investigating the quantitative impact of visual de...

Context-agnostic machine learning for Parkinson's disease motor symptom detection using wearable sensors.

BACKGROUND: Parkinson's disease is a rapidly growing neurodegenerative disorder with various motor and non-motor symptoms, affecting millions of peopl...

Techniques, Performance, and Feasibility of Natural Language Processing for Abstract Screening in Evidence Synthesis: A Systematic Review.

BACKGROUND: Natural language processing (NLP) techniques offer promising solutions for semi-automating the time-consuming process of abstract screenin...

BoltzOmics: Predicting genetic variant effects on drug binding with Boltz-2.

A mechanistic understanding of how genetic variants alter drug-receptor binding is central to precision medicine, drug response prediction, and drug d...

AI-Simulated Patients for Training Shared Decision-Making: Feasibility Study in Medical Education.

BACKGROUND: Shared decision-making (SDM) is a key element of patient-centered care; however, opportunities for structured and scalable SDM training re...

An optimized deep learning-based drug recommendation system using a sentiment analysis and transformer models for improved predictions.

BACKGROUND: Healthcare users can be assisted by a medicine or drug recommendation system (MRS) by understanding their needs and supporting informed de...

LINKER: Learning Interactions between Functional Groups and Residues with Chemical Knowledge-Enhanced Reasoning and Explainability.

Accurate identification of interactions between protein residues and ligand functional groups is critical for understanding molecular recognition and ...

Using Artificial Intelligence to Identify Patterns and Predictors of Adverse Drug Reactions in Cardiovascular Patients: A Comprehensive Narrative Review.

Artificial intelligence (AI) has emerged as a transformative tool for improving the detection, prediction, and prevention of adverse drug reactions (A...

Machine Learning-Augmented Traditional Analysis of Lactate vs Lactate-to-Albumin Ratio for Predicting Mortality Risk in Patients With Sepsis: Large-Scale Retrospective Study.

BACKGROUND: Effective risk stratification in sepsis remains a critical clinical challenge. Serum lactate is a cornerstone biomarker of metabolic dysfu...

AI for Causality Assessment in Pharmacovigilance: Protocol for a Scoping Review.

BACKGROUND: Pharmacovigilance aims to protect patient safety by identifying and managing adverse events associated with pharmaceuticals. Determining t...

Personalised approach to hypertension treatment: protocol for the HYPERMARKER randomised controlled trial.

INTRODUCTION: Blood pressure treatment response is variable in individual patients, and the choice of medical therapy is often dependent on clinician ...

A Noise-Aware Robustness Evaluation Framework for Breast Cancer Classification in Ultrasound Imaging.

Accurate detection of breast cancer is essential, as it remains a leading cause of cancer-related mortality worldwide. Ultrasound is adopted due to it...