Prescriptions

Modeling Personalized Difficulty of Rehabilitation Exercises Using Causal Trees

Rehabilitation robots are often used in game-like interactions for rehabilitation to increase a person's motivation to complete rehabilitation exercises. By adjusting exercise difficulty for a specific user throughout the exercise interaction, robots can maximize both the user's rehabilitation outcomes and the their motivation throughout the exercise. Previous approaches have assumed exercises h...

GASCADE: Grouped Summarization of Adverse Drug Event for Enhanced Cancer Pharmacovigilance

In the realm of cancer treatment, summarizing adverse drug events (ADEs) reported by patients using prescribed drugs is crucial for enhancing pharmacovigilance practices and improving drug-related decision-making. While the volume and complexity of pharmacovigilance data have increased, existing research in this field has predominantly focused on general diseases rather than specifically address...

EOPose : Exemplar-based object reposing using Generalized Pose Correspondences

Reposing objects in images has a myriad of applications, especially for e-commerce where several variants of product images need to be produced quic...

End-to-end fully-binarized network design: from Generic Learned Thermometer to Block Pruning

Existing works on Binary Neural Network (BNN) mainly focus on model's weights and activations while discarding considerations on the input raw data....

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos

Parkinson's disease (PD) is a neurodegenerative disorder, manifesting with motor and non-motor symptoms. Depressive symptoms are prevalent in PD, af...

Beyond the Monitor: Mixed Reality Visualization and AI for Enhanced Digital Pathology Workflow

Pathologists rely on gigapixel whole-slide images (WSIs) to diagnose diseases like cancer, yet current digital pathology tools hinder diagnosis. The...

Structure Causal Models and LLMs Integration in Medical Visual Question Answering

Medical Visual Question Answering (MedVQA) aims to answer medical questions according to medical images. However, the complexity of medical data lea...

A Survey on Progress in LLM Alignment from the Perspective of Reward Design

The alignment of large language models (LLMs) with human values and intentions represents a core challenge in current AI research, where reward mech...

Automated Parsing of Engineering Drawings for Structured Information Extraction Using a Fine-tuned Document Understanding Transformer

Accurate extraction of key information from 2D engineering drawings is crucial for high-precision manufacturing. Manual extraction is time-consuming...

CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering

Unsupervised anomaly detection (UAD) seeks to localize the anomaly mask of an input image with respect to normal samples. Either by reconstructing n...

On the Importance of Gaussianizing Representations

The normal distribution plays a central role in information theory - it is at the same time the best-case signal and worst-case noise distribution, ...

Model order reduction of hemodynamics by space-time reduced basis and reduced fluid-structure interaction

In this work, we apply the space-time Galerkin reduced basis (ST-GRB) method to a reduced fluid-structure interaction model, for the numerical simul...

AdCare-VLM: Leveraging Large Vision Language Model (LVLM) to Monitor Long-Term Medication Adherence and Care

Chronic diseases, including diabetes, hypertension, asthma, HIV-AIDS, epilepsy, and tuberculosis, necessitate rigorous adherence to medication to av...

In Silico Research Is Rewriting the Rules of Drug Development: Is It the End of Human Trials?

Recent advances in computational biology, artificial intelligence (AI), and regulatory science are rapidly displacing the traditional reliance on anim...

In silico analysis of soybean-derived umami peptides: Discovery and interaction mechanisms with T1R1/T1R3 receptor.

In this study, umami peptides with binding activity to the umami receptor T1R1/T1R3 were screened and identified from soybean protein. Using virtual e...

Large-scale information retrieval and correction of noisy pharmacogenomic datasets through residual thresholded deep matrix factorization.

Pharmacogenomics studies are attracting an increasing amount of interest from researchers in precision medicine. The advances in high-throughput exper...

Gated-GPS: enhancing protein-protein interaction site prediction with scalable learning and imbalance-aware optimization.

In protein-protein interaction site (PPIS) prediction, existing machine learning models struggle with small datasets, limiting their predictive accura...

An inherently interpretable AI model improves screening speed and accuracy for early diabetic retinopathy.

Diabetic retinopathy (DR) is a frequent complication of diabetes, affecting millions worldwide. Screening for this disease based on fundus images has ...

Artificial intelligence to predict inhibitors of drug-metabolizing enzymes and transporters for safer drug design.

INTRODUCTION: Drug-metabolizing enzymes (DMEs) and transporters (DTs) play integral roles in drug metabolism and drug-drug interactions (DDIs) which d...

Patient Perception of Artificial Intelligence Use in Interpretation of Screening Mammograms: A Survey Study.

Purpose To assess patient perceptions of artificial intelligence (AI) use in the interpretation of screening mammograms. Materials and Methods In a pr...