Prescriptions

Artificial Intelligence Impact on Radiologist Performance in Breast Cancer Screening with Digital Breast Tomosynthesis.

PURPOSE: Assess impact of artificial intelligence (AI) on radiologists' detection of cancer on digital breast tomosynthesis (DBT) exams based on density, size, stage, and histopathology. METHODS AND MATERIALS: Retrospective analysis of mammography audit data and screening cancers was conducted at four sites during two time periods with nine dedicated breast radiologists. Data was collected from Ma...

Interpretable Ensemble Machine Learning Prediction of Nonadherence and the Risk of Nonpersistence of Targeted Disease-Modifying Antirheumatic Agents in Older Adults With Rheumatoid Arthritis.

PURPOSE: Ensemble machine learning (ML) demonstrated potential for improving predictions based on big health care data. We developed and validated interpretable ensemble ML models in evaluating the nonadherence and nonpersistence of biological or targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) in rheumatoid arthritis (RA). METHODS: This retrospective study used 5% Medicare cl...

Deep learning-based H&E-derived risk scores in colorectal cancer: associations with tumour morphology, biology, and predicted drug response.

Over recent years, several deep learning (DL) models have been presented to predict colorectal cancer (CRC) patient survival directly from haematoxyli...

Expanding access to cerebrovascular imaging with routine MRI for pre-surgical assessment of intracranial tumor patients: an AI model development and multi-center validation study.

BACKGROUND: Angiography is the gold standard for assessing the relationship between cerebral arteries and intracranial tumors, but its use is limited ...

[Drug repositioning prediction based on dynamic feature learning on heterogeneous graphs].

OBJECTIVES: To address the challenges faced by existing artificial intelligence methods in modeling complex heterogeneous biological networks, particu...

Electroencephalography-Based Machine Learning Models for Predicting Ketogenic Diet Outcomes in Pediatric Drug-Resistant Epilepsy.

BACKGROUND: Ketogenic diet therapy (KDT) is an established treatment for drug-resistant epilepsy (DRE); however, methods for predicting its effectiven...

Privacy-preserving multimodal fusion for Alzheimer's staging: A federated vision transformer framework with explainable AI.

Accurate, early-stage staging of Alzheimer's disease (AD) is critical for therapeutic intervention but is hampered by data privacy regulations, multim...

RCLG-PPIS: Structure-Aware Protein-Protein Interaction Site Prediction with Cascaded Local and Global Information.

Accurately obtaining Protein-protein Interaction Sites (PPIS) information is crucial for understanding cell functions and drug development. In recent ...

Generative AI in preanesthetic consultations: Effects on efficiency, documentation workload, quality, and physician-patient interaction: A simulation trial.

BACKGROUND: Clinicians spend over 30% of their workday on electronic health records, reducing patient interaction and contributing to burnout. Preanes...

YOLO-based real-time floating debris counting in urban rivers for flood monitoring and water resource management.

Urban flooding is increasingly exacerbated by the accumulation of floating debris in rivers, which obstructs water flow, degrades water quality, and p...

Discovery of potent ALK tyrosine kinase inhibitors for thyroid cancer via machine learning modeling, molecular docking, MD simulations, and DFT study.

The ever-increasing need for effective therapeutic management of thyroid cancer (TC) necessitates the exploration of novel approaches for advanced dru...

Prediction of bacterial protein-compound interactions with only positive samples.

MOTIVATION: Prediction of Compound-Protein Interactions (CPI) in bacteria is crucial to advance various pharmaceutical and chemical engineering fields...

A framework for enhancing pharmaceutical integrity and patient safety: novel mobile health solution integrating smart packaging and computer vision.

Counterfeit and substandard pharmaceuticals represent a critical global health crisis, with the World Health Organisation (WHO) reporting that falsifi...

Anomaly detection of cybersecurity behavior using cross-sequence aligned transformer-A dynamic recognition approach for high-frequency interaction patterns.

In high-frequency interaction network environments, network traffic features and user behavior sequences often exhibit pronounced temporal asynchrony ...

Shaping the future: A pilot study on how AI-powered chatbots shape patient perceptions of pharmacist roles.

OBJECTIVE: To investigate how AI-powered chatbots influence patient perceptions of pharmacist roles compared to traditional educational methods and ev...

Hypoglycemia problem-solving ability as a key predictor of severe hypoglycemia: a machine learning approach in adults with type 1 diabetes.

AIMS/INTRODUCTION: Severe hypoglycemia (SH) is a major complication in adults with type 1 diabetes mellitus (T1DM). The multifactorial etiology of T1D...

Predicting mycophenolic acid exposure in pediatric lupus nephritis: a real-world study across three clinical scenarios.

OBJECTIVES: Childhood-onset systemic lupus erythematosus (cSLE) with lupus nephritis (LN) presents therapeutic challenges due to complex mycophenolic ...

An online machine learning model for predicting medication adherence in hypertensive patients: data from the China health and retirement longitudinal study (CHARLS).

INTRODUCTION: Hypertension, a leading global cause of death with high prevalence and poor control, faces a critical issue of poor medication adherence...

How to undertake oral medicine dosage calculations.

RATIONALE AND KEY POINTS: Safe and effective medicine administration and dosage calculation are fundamental nursing proficiencies. Medicine dosage cal...

Computational Drug Repurposing for Alzheimer's Disease via Sheaf Theoretic Population-Scale Analysis of snRNA-Seq Data.

Single-cell and single-nucleus RNA sequencing are used to reveal heterogeneity in cells, showing a growing potential for precision and personalized me...