Prescriptions

Detecting Adverse Drug Events in Clinical Notes Using Large Language Models.

Monitoring adverse drug events (ADEs) is critical for pharmacovigilance and patient safety. However, identifying ADEs remains challenging, as suspected or confirmed side effects are often documented solely in the unstructured text of electronic health records (EHRs). Manually reviewing clinical notes to detect ADEs is labor-intensive and time-consuming, highlighting the need for automated methods ...

Extracting Pediatric Information from Summaries of Product Characterics with a Large Language Model and No-Code.

Accurate medication information is important for children, as dosing errors can have severe consequences compared to adults. We propose an automated method to extract pediatric information from Summaries of Product Characteristics (SPC). We used AirOps, a commercial and no-code visual editor to implement a processing chain with large language model GPT-4o mini on 50 SPCs. The task focused on evalu...

GCN-Transformer: Graph Convolutional Network and Transformer for Multi-Person Pose Forecasting Using Sensor-Based Motion Data.

Multi-person pose forecasting involves predicting the future body poses of multiple individuals over time, involving complex movement dynamics and int...

VeriFact: Enhancing Long-Form Factuality Evaluation with Refined Fact Extraction and Reference Facts

Large language models (LLMs) excel at generating long-form responses, but evaluating their factuality remains challenging due to complex inter-sente...

Fate-tox: fragment attention transformer for E(3)-equivariant multi-organ toxicity prediction.

Toxicity is a critical hurdle in drug development, often causing the late-stage failure of promising compounds. Existing computational prediction mode...

The Patient Prescription for Integrating Artificial Intelligence Into Gastroenterology Care.

Integrating Dynamic Red Blood Cell Distribution Width Monitoring and β-Blocker Therapy for Mortality Prediction in Intensive Care Unit Cardiomyopathy Patients: A Bayesian Multivariate Joint Model and Machine Learning Study.

Cardiomyopathy is a key cause of cardiovascular mortality in critically ill patients. Although red blood cell distribution width (RDW) is recognized ...

The Role of Artificial Intelligence in Drug Discovery and Pharmaceutical Development: A Paradigm Shift in the History of Pharmaceutical Industries.

In today's world, with an increasing patient population, the need for medications is increasing rapidly. However, the current practice of drug develop...

Effectiveness of eHealth for Medication Adherence in Renal Transplant Recipients: Systematic Review and Meta-Analysis.

BACKGROUND: As the optimal treatment for end-stage renal disease, kidney transplantation has proven instrumental in enhancing patient survival and qua...

A modernized approach to sentiment analysis of product reviews using BiGRU and RNN based LSTM deep learning models.

With the advent of Web 2.0 and popularization of online shopping applications, there has been a huge upsurge of user generated content in recent times...

Predicting antiseizure medication response in newly diagnosed epilepsy using quantitative EEG and machine learning.

BACKGROUND: Predicting long-term outcomes in newly diagnosed epilepsy remains limited by reliance on clinical features and visual EEG interpretation. ...

Transcriptomic Profiling of Hypoxia-Adaptive Responses in Tibetan Goat Fibroblasts.

The Tibetan goat () exhibits remarkable adaptations to high-altitude hypoxia, yet the molecular mechanisms remain unclear. This study integrates RNA-s...

Identifying Mild-to-Moderate Atopic Dermatitis Using a Generic Machine Learning Approach: A Danish National Health Register Study.

Atopic dermatitis is a chronic skin disease, causing itching and recurrent eczematous lesions. In Danish national register data, adults with atopic de...

Supervised machine learning for predicting drug release from acetalated dextran nanofibers.

Electrospun drug-loaded polymeric nanofibers can improve the efficacy of therapeutics for a variety of implications. By design, these biomaterial plat...

Effect of Cell-Cell Interaction on Single-Cell Behavior Revealed by a Deep Learning-Aided High-Throughput Addressable Single-Cell Coculture System.

Cell-cell interactions are crucial for understanding various physiological and pathological processes, yet conventional population-level methods fail ...

Active Learning-Based Prediction of Drug Combination Efficacy.

Combination therapy, which involves the use of multiple drugs, has emerged as a promising approach to cancer treatment. However, traditional combinati...

In Silico Prediction and Validation of LmGt Inhibitors Using QSAR and Molecular Docking Approaches

Leishmaniasis caused by Leishmania mexicana relies on Leishmania mexicana gluscose transporter (LmGT) receptors, which play an important role in glu...

Reproducibility, Replicability, and Insights into Visual Document Retrieval with Late Interaction

Visual Document Retrieval (VDR) is an emerging research area that focuses on encoding and retrieving document images directly, bypassing the depende...

Towards user-centered interactive medical image segmentation in VR with an assistive AI agent

Crucial in disease analysis and surgical planning, manual segmentation of volumetric medical scans (e.g. MRI, CT) is laborious, error-prone, and cha...

DrIVeNN: Drug Interaction Vectors Neural Network.

Polypharmacy, the concurrent use of multiple drugs to treat a single condition, is common in patients managing multiple or complex conditions. However...