Prescriptions

The predictive edge: modeling and simulation in drug product development.

It is well-known that drug development is challenging and a time- and resource-intensive endeavor. Historically, it has relied heavily on trial-and-error, empirical approaches that yield a low probability of success. Despite continuous efforts to improve efficiency across the development stages the overall success rate from clinical trial initiation to market approval remains low. In response to t...

Brain aging in bipolar disorder using a neuroimaging and machine learning-derived metric: Findings from the ENIGMA BD Working Group.

BACKGROUND: Bipolar disorder (BD) is associated with clinical and biological markers of premature aging. In this largest study of brain age in BD to date, with 2919 participants, we compared brain-predicted age difference (brain-PAD) in individuals with BD and healthy comparison (HC) participants. Brain-PAD is a machine learning-estimated metric that quantifies the difference between an individual...

Development and temporal validation of machine learning models for predicting clinically relevant medication reconciliation discrepancies at the emergency department: A single-center retrospective study.

OBJECTIVE: Medication discrepancies at hospital admission are common and can cause preventable patient harm. Predictive models can help prioritize med...

Improving medication error classification using a reasoning large language model.

OBJECTIVES: To assess the performance of a reasoning large language model (LLM) in identifying medication errors in medical incident reports. MATERIAL...

Multimodal Rehabilitation for Advanced Cancer Pain: a Narrative Review of Emerging Nonpharmacological Strategies.

PURPOSE OF REVIEW: This narrative review aims to explore research advances in multimodal rehabilitation for advanced cancer pain, with a primary focus...

Breast cancer frontiers: mapping molecular signals, intelligent diagnostics, and adaptive therapies.

Breast cancer continues to be a significant worldwide health concern, requiring ongoing improvements in early detection, therapeutic approaches, and c...

Recent advances in redox-responsive polysaccharide-based nanostructures for cancer therapy.

Conventional tumor chemotherapy faces limitations including drug resistance, high toxicity, non-selectivity, and side effects. Nano-drug delivery syst...

Performance of Machine Learning Models in Predicting Outcomes After ACDF: A Systematic Review and Meta-Analysis of 443 000 Patients.

Study DesignSystematic review and meta-analysis.ObjectiveDirect head-to-head comparison of machine learning models aiming to predict outcomes in Anter...

Multidimensional analysis and predictive modeling of cognitive decline risk in the United States using propensity score matching and machine learning.

Cognitive decline, an early indicator of neurodegenerative disorders, presents a growing public health challenge. This study aimed to integrate causal...

Relation-aware pre-trained network with hierarchical aggregation mechanism for cold-start drug recommendation.

Drug recommendation systems have garnered considerable interest in the healthcare, striving to offer precise and customized drug prescriptions that al...

HybridDeepSynergy: A hybrid deep learning model integrating CNN, LSTM, and attention mechanisms for cancer drug synergy prediction.

A variety of AI-based approaches have been employed to analyze complex genomic datasets. Predicting the synergy of drug combinations is a critical ste...

An integrative multiomics random forest framework for robust biomarker discovery.

BACKGROUND: High-throughput technologies now produce a wide array of omics data, from genomic and transcriptomic profiles to epigenomic and proteomic ...

Machine Learning Model Predicts Recurrent Clostridioides difficile Infection in Patients With Inflammatory Bowel Disease (Recur CDI-IBD).

INTRODUCTION: Clostridioides difficile infection (CDI) present a significant challenge in patients with inflammatory bowel disease (IBD), with high re...

CompBind: Complex Guided Pretraining-Based Structure-Free Protein-Ligand Affinity Prediction.

Accurate prediction of protein-ligand binding affinity is essential in drug discovery. However, the limited availability and high cost of experimental...

PTET-DDI: Dual-Channel Drug-Drug Interaction Prediction with a Pre-Trained Language Model and Equivariant Graph Transformer.

Combination therapy is widely used in clinical practice, rendering accurate prediction of drug-drug interactions (DDIs) essential for treatment safety...

Psychiatric Documentation and Management in Primary Care With Artificial Intelligence Scribe Use.

IMPORTANCE: Despite increasingly widespread use of artificial intelligence (AI)-driven ambient scribes in medicine, the extent to which they are assoc...

An Explainable Molecular Token Estimation Method for Knowledge-aware Drug-Drug Interaction Prediction.

In molecular representation learning (MRL), tokens (e.g., atoms, motifs, and fingerprints) are the basic elements to represent molecules. It is a comm...

Machine Learning Prediction of Pharmacogenetic Testing Uptake Among Opioid-Prescribed Patients Using Electronic Health Records: Retrospective Cohort Study.

BACKGROUND: Opioids are a widely prescribed class of medication for pain management. However, they have variable efficacy and adverse effects among pa...

Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity.

BACKGROUND: Despite increased understanding of psoriasis pathogenesis, molecular classification of clinical phenotypes and disease severity is poorly ...

Impact of patient communication style on agentic AI-generated clinical advice in E-medicine.

BACKGROUND: E-medicine use has surged, and health systems are exploring large language models (LLMs) for message triage. However, it is still unknown ...