Prescriptions

Graph-patchformer: Patch interaction transformer with adaptive graph learning for multivariate time series forecasting.

Multivariate time series (MTS) forecasting plays a pivotal role in the digitalization and intelligent development of modern society, while previous MTS forecasting methods based on deep learning often rely on capturing intra-series dependencies for modeling, neglecting the structural information within MTS and failing to consider inter-series local dynamic dependencies. Although some approaches ut...

Development and validation of a breast cancer survival prediction model based on perioperative anesthesia-related drug target genes and analysis of immune microenvironment and drug sensitivity.

INTRODUCTION: This study aimed to create a survival prediction model for breast cancer(BC) using perioperative anesthesia - related drug target genes(PARDTGs). It explored their immune microenvironment and drug sensitivity for personalized therapy. METHODS: Transcriptomic sequencing data of BC were downloaded from The Cancer Genome Atlas (TCGA) database. Common PARDTGs were retrieved from the Drug...

Machine learning models for predicting recurrence and malignant transformation of oral leukoplakia.

OBJECTIVE: This study aimed to evaluate machine learning models for predicting the recurrence and malignant transformation of oral leukoplakia (OL). M...

Comparison of ChatGPT-3.5, ChatGPT-4.0 and DeepSeek in generating dietary plans for patients with chronic kidney disease: A focus on nutritional accuracy and dietary inflammation.

BACKGROUND: Our previous research indicated that ChatGPT-3.5 was inadequate in generating nutritionally accurate dietary plans for patients with chron...

Interrelations between dopaminergic-, gabaergic- and glutamatergic neurotransmitters in antipsychotic-naïve psychosis patients and the association to initial treatment response.

Preclinical evidence points to disturbances in neural networks in psychosis involving interrelations between dopaminergic-, GABAergic- and glutamaterg...

MINIGE-MNER: A multi-stage interaction network inspired by gene editing for multimodal named entity recognition.

Multimodal Named Entity Recognition (MNER) integrates complementary information from both text and images to identify named entities within text. Howe...

The prognostic significance of metabolic score for insulin resistance in evaluating all-cause and cardiovascular mortality risk among individuals across stages 1-4 of cardiovascular-kidney-metabolic syndrome: a nationwide prospective cohort study.

BACKGROUND AND AIM: Cardiovascular-kidney-metabolic syndrome (CKM) embodies the intricate interaction among metabolic, kidney, and cardiovascular dysf...

Advances in high-throughput drug screening based on pharmacotranscriptomics.

BACKGROUND: Drug screening constitutes the predominant paradigm for novel drug discovery. With the development of omics, drug screening has gradually ...

HMT-DTI: Hierarchical meta-path learning with transformer for drug-target interaction prediction.

Drug-target interaction (DTI) prediction plays a crucial role in drug discovery and repurposing by efficiently and accurately identifying potential th...

Leveraging Large Language Models in Extracting Drug Safety Information from Prescription Drug Labels.

INTRODUCTION: Adverse drug reactions (ADRs), including those resulting from drug interactions, remain a leading cause of morbidity and mortality. Stru...

Identifying meaningful drug response biomarkers from public pharmacogenomic datasets with biologically informed interpretable neural networks.

With the advancements of next-generation sequencing, publicly available pharmacogenomic datasets from cancer cell lines provide a handle for developin...

Pharmacological Haemodynamic Management in the Intensive Care Unit: The Evolution of the Nurse's Role Over 50 Years.

AIM: To examine the evolution of intensive care nurses' roles in pharmacological haemodynamic management from 1975 to 2025 and to explore projected re...

Beyond black boxes: using explainable causal artificial intelligence to separate signal from noise in pharmacovigilance.

Artificial intelligence (AI), particularly machine learning (ML), is increasingly influencing pharmacovigilance (PV) by improving case triage and sign...

Optimizing guideline-directed medical therapy in chronic kidney disease and type 2 diabetes mellitus patients in primary care.

BACKGROUND: Despite KDIGO (Kidney Disease: Improving Global Outcomes) recommendations for renin-angiotensin-aldosterone system inhibitors (RAASi's) an...

Explainable machine learning for discontinuation of therapeutic antibiotics in intensive care patients.

INTRODUCTION: The threshold for initiating lifesaving antibiotics for intensive care patients is low while determining when to stop remains challengin...

An interpretable machine learning approach to evaluate 30-day mortality risk in patients with community-onset bacteremia.

BACKGROUND: Machine learning (ML) techniques are increasingly being used in health outcome research to develop predictive models. However, ML models a...

OCR: OmniNet-Fusion: A hybrid attention-based CNN-RNN model for multi-omics integration in precision cancer drug response prediction.

The growing complexity of cancer therapeutics challenges the use of state-of-the-art computational models for drug response prediction. Design and imp...

Navigating Medical Device Safety: Current Status, Challenges, and Future Regulatory Directions.

Medical devices are indispensable in modern healthcare. They enable the prevention, diagnosis, and treatment of diseases while enhancing patient outco...

Use of Machine Learning to Derive Discrete Clinical Phenotypes and Assess Treatment-Effect Heterogeneity in the Steroids in Cardiac Surgery Trial Dataset.

BACKGROUND: Robust clinical trial data provide a key component for the development of evidence-informed medicine. However, clinical trial data may dem...

Artificial intelligence in pharmacovigilance signal management: a review of tools, implementations, research, and regulatory landscape.

INTRODUCTION: The integration of artificial intelligence (AI) into pharmacovigilance (PV) has advanced rapidly in recent years. AI tools have the pote...