Prescriptions

Toward Opioid-Free Ambulatory Surgery: A Prospective Study Using Machine Learning to Predict Postoperative Opioid Use.

BACKGROUND: Postoperative opioid use has the risk of dependence and diversion. We developed an opioid-sparing regimen and identified factors associated with postoperative opioid use. STUDY DESIGN: The Toward Opioid-Free Ambulatory Surgery program was developed by establishing a regimen of ibuprofen 600 mg and acetaminophen 650 mg, alternating every 3 hours, with a rescue prescription of oxycodone ...

Etiologic diagnosis of seasonal allergic rhinitis supported by artificial intelligence: the @IT-2020 project.

BACKGROUND: A precise etiological diagnosis of seasonal allergic rhinitis (SAR) is essential for a tailored prescription of its only curative treatment, allergen-specific immunotherapy (AIT). This is a challenging task in temperate climates, where most patients are polysensitized to multiple pollen with overlapping seasons. OBJECTIVE: The study aims to develop a modular, flexible and validated Cli...

LogoXpertNet: a novel lightweight logo classification using deep learning.

Logo classification is crucial in various applications, including brand monitoring, copyright protection, and digital forensics. Traditional computer ...

A Comprehensive Review of the Revolutionary Potential of Blockchain in Safe, Secure, and Sustainable Pharmaceutical Operations.

BACKGROUND: Pharmaceutical research and industrial operations generate vast volumes of sensitive data across drug discovery, formulation development, ...

Enhancing drug synergy in malignant diseases with deep architecture optimization algorithms.

Malignant diseases remain one of the leading causes of death globally. Drug synergy has emerged as an effective approach for treating malignancy, offe...

DeepLMI: Deep Feature Mining with a Globally Enhanced Graph Convolutional Network for Robust lncRNA-miRNA Interaction Prediction.

MOTIVATION: Interactions between long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) play pivotal roles in gene regulation and disease progression, ...

SPARTAN: automated table detection and extraction from documents using advanced OpenCV heuristics and OCR techniques.

The rapid growth of born-digital PDF documents has amplified the demand for fast, precise tabular data extraction on an industrial scale. State-of-the...

Explainable drug side effect prediction in central neural system via biologically informed graph neural network.

The early detection of potential side effects (SEs) is a critical yet formidable challenge within the realms of drug development and patient healthcar...

Bone Metastasis Detection at CT with Deep Learning Models Trained Using Multicenter, Multimodal Reference Standards: Development and Evaluation.

Purpose To develop and validate deep learning models for detecting bone metastases on abdominal and thoracic CT scans, considering lesion visibility, ...

Transformer-based deep learning model for real-time prediction of intraoperative hypotension using dynamic time-series vital signs: A retrospective study.

BACKGROUND: The clinical importance of transient intraoperative hypotension (IOH) remains debated, and existing models often rely on high-resolution w...

AI-generated Feedback Following Social Robotic Virtual Patient Interactions and Medical Student Performance: Nonrandomized Quasi-Experimental Study.

BACKGROUND: Virtual patients (VPs) demonstrate effectiveness in improving clinical reasoning skills; however, traditional VP platforms often lack indi...

DeepSTFSynergy: A multi-scale structural information fusion method for personalized drug combination prediction.

Efficiently predicting drug synergy is crucial for developing personalized cancer combination therapy regimens. However, existing methods primarily fo...

OptiSyn: an interpretable, multi-omics-driven graph convolutional network framework for synergy-oriented drug combination design in disease treatment.

BACKGROUND: Bioinformatics and large-scale computational modelling have emerged as essential research fields in modern biomedical science, enabling dr...

Multi-output LSTM-based prediction of postoperative delirium: integrating baseline and perioperative data for enhanced risk stratification in older spine surgery patients.

INTRODUCTION: Postoperative delirium (POD) adversely affects clinical outcomes among older adults undergoing spine surgery. However, existing predicti...

Predicting CT-based coronal plane knee phenotype parameters using imageless navigation and machine learning.

BACKGROUND: Coronal plane alignment of the knee (CPAK) categorizes knee phenotypes according to joint line obliquity (JLO) and the arithmetic hip‒knee...

PepLM-GNN: A graph neural network framework leveraging pre-trained language models for peptide-protein binding prediction.

MOTIVATION: The precise prediction of peptide-protein interaction (PepPI) is a core support for promoting breakthroughs in peptide drug research, as w...

Physics-based machine learning for enhanced drug formulation development.

Formulation design is constrained by scarce and heterogeneous experimental data, which limits the accuracy and generalizability of conventional AI mod...

Digital twins to accelerate target identification and drug development for immune-mediated disorders.

Immune-related/mediated disorders (IDs) comprise a very diverse group of diseases affecting millions worldwide. The complexity and heterogeneity of ID...

TCR recognition of peptide-HLA class II complexes in autoimmunity: Structural insights for therapeutic targeting.

Autoimmune diseases encompass a broad spectrum of disorders in which self-reactive T and B cells breach immune tolerance and drive chronic tissue infl...

Assessing the accuracy of 4 generative AI tools to guide dosing of long-acting injectable antipsychotics.

PURPOSE: The purpose of this study was to review the accuracy of 4 different artificial intelligence (AI) tools in providing dosing recommendations fo...