Product Alert

Task as Context Prompting for Accurate Medical Symptom Coding Using Large Language Models

Accurate medical symptom coding from unstructured clinical text, such as vaccine safety reports, is a critical task with applications in pharmacovigilance and safety monitoring. Symptom coding, as tailored in this study, involves identifying and linking nuanced symptom mentions to standardized vocabularies like MedDRA, differentiating it from broader medical coding tasks. Traditional approaches ...

Human-Centered Development of an Explainable AI Framework for Real-Time Surgical Risk Surveillance

Background: Artificial Intelligence (AI) clinical decision support (CDS) systems have the potential to augment surgical risk assessments, but successful adoption depends on an understanding of end-user needs and current workflows. This study reports the initial co-design of MySurgeryRisk, an AI CDS tool to predict the risk of nine post-operative complications in surgical patients. Methods: Semi-...

BiSeg-SAM: Weakly-Supervised Post-Processing Framework for Boosting Binary Segmentation in Segment Anything Models

Accurate segmentation of polyps and skin lesions is essential for diagnosing colorectal and skin cancers. While various segmentation methods for pol...

Timely Trajectory Reconstruction in Finite Buffer Remote Tracking Systems

Remote tracking systems play a critical role in applications such as IoT, monitoring, surveillance and healthcare. In such systems, maintaining both...

Sim-is-More: Randomizing HW-NAS with Synthetic Devices

Existing hardware-aware NAS (HW-NAS) methods typically assume access to precise information circa the target device, either via analytical approxima...

Multimodal LLMs for OCR, OCR Post-Correction, and Named Entity Recognition in Historical Documents

We explore how multimodal Large Language Models (mLLMs) can help researchers transcribe historical documents, extract relevant historical informatio...

A Scalable Predictive Modelling Approach to Identifying Duplicate Adverse Event Reports for Drugs and Vaccines

The practice of pharmacovigilance relies on large databases of individual case safety reports to detect and evaluate potential new causal associatio...

Diagnosis of Pulmonary Hypertension by Integrating Multimodal Data with a Hybrid Graph Convolutional and Transformer Network

Early and accurate diagnosis of pulmonary hypertension (PH) is essential for optimal patient management. Differentiating between pre-capillary and p...

Post-Incorporating Code Structural Knowledge into LLMs via In-Context Learning for Code Translation

Code translation migrates codebases across programming languages. Recently, large language models (LLMs) have achieved significant advancements in s...

Ontology-based Semantic Similarity Measures for Clustering Medical Concepts in Drug Safety

Semantic similarity measures (SSMs) are widely used in biomedical research but remain underutilized in pharmacovigilance. This study evaluates six o...

PVLens: Enhancing Pharmacovigilance Through Automated Label Extraction

Reliable drug safety reference databases are essential for pharmacovigilance, yet existing resources like SIDER are outdated and static. We introduc...

M$^2$CD: A Unified MultiModal Framework for Optical-SAR Change Detection with Mixture of Experts and Self-Distillation

Most existing change detection (CD) methods focus on optical images captured at different times, and deep learning (DL) has achieved remarkable succ...

Reason2Attack: Jailbreaking Text-to-Image Models via LLM Reasoning

Text-to-Image(T2I) models typically deploy safety filters to prevent the generation of sensitive images. Unfortunately, recent jailbreaking attack m...

Improving Quantization with Post-Training Model Expansion

The size of a model has been a strong predictor of its quality, as well as its cost. As such, the trade-off between model cost and quality has been ...

Preferential Multi-Objective Bayesian Optimization for Drug Discovery

Despite decades of advancements in automated ligand screening, large-scale drug discovery remains resource-intensive and requires post-processing hi...

Post-composing ontology terms for efficient phenotyping in plant breeding.

Ontologies are widely used in databases to standardize data, improving data quality, integration, and ease of comparison. Within ontologies tailored t...

QuartDepth: Post-Training Quantization for Real-Time Depth Estimation on the Edge

Monocular Depth Estimation (MDE) has emerged as a pivotal task in computer vision, supporting numerous real-world applications. However, deploying a...

Inducing Causal Structure for Interpretable Neural Networks Applied to Glucose Prediction for T1DM Patients

Causal abstraction techniques such as Interchange Intervention Training (IIT) have been proposed to infuse neural network with expert knowledge enco...

Combined impact of grey and superficial white matter abnormalities: implications for epilepsy surgery

Drug-resistant focal epilepsy is associated with abnormalities in the brain in both grey matter (GM) and superficial white matter (SWM). However, it...

ACT360: An Efficient 360-Degree Action Detection and Summarization Framework for Mission-Critical Training and Debriefing

Effective training and debriefing are critical in high-stakes, mission-critical environments such as disaster response, military simulations, and in...