Information Technology

Anomaly Detection and Generation with Diffusion Models: A Survey

Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing, by identifying unexpected patterns that deviate from established norms in real-world data. Recent advancements in deep learning, specifically diffusion models (DMs), have sparked significant interest due to their ability to learn complex data distributio...

Are Trees Really Green? A Detection Approach of IoT Malware Attacks

Nowadays, the Internet of Things (IoT) is widely employed, and its usage is growing exponentially because it facilitates remote monitoring, predictive maintenance, and data-driven decision making, especially in the healthcare and industrial sectors. However, IoT devices remain vulnerable due to their resource constraints and difficulty in applying security patches. Consequently, various cybersec...

Enhanced Consistency Bi-directional GAN(CBiGAN) for Malware Anomaly Detection

Static analysis, a cornerstone technique in cybersecurity, offers a noninvasive method for detecting malware by analyzing dormant software without e...

Fuse and Federate: Enhancing EV Charging Station Security with Multimodal Fusion and Federated Learning

The rapid global adoption of electric vehicles (EVs) has established electric vehicle supply equipment (EVSE) as a critical component of smart grid ...

SAFER: A Calibrated Risk-Aware Multimodal Recommendation Model for Dynamic Treatment Regimes

Dynamic treatment regimes (DTRs) are critical to precision medicine, optimizing long-term outcomes through personalized, real-time decision-making i...

ICU-TSB: A Benchmark for Temporal Patient Representation Learning for Unsupervised Stratification into Patient Cohorts

Patient stratification identifying clinically meaningful subgroups is essential for advancing personalized medicine through improved diagnostics and...

UTSA-NLP at ArchEHR-QA 2025: Improving EHR Question Answering via Self-Consistency Prompting

We describe our system for the ArchEHR-QA Shared Task on answering clinical questions using electronic health records (EHRs). Our approach uses larg...

From EHRs to Patient Pathways: Scalable Modeling of Longitudinal Health Trajectories with LLMs

Healthcare systems face significant challenges in managing and interpreting vast, heterogeneous patient data for personalized care. Existing approac...

A Dataset for Addressing Patient's Information Needs related to Clinical Course of Hospitalization

Patients have distinct information needs about their hospitalization that can be addressed using clinical evidence from electronic health records (E...

AI Agents for Conversational Patient Triage: Preliminary Simulation-Based Evaluation with Real-World EHR Data

Background: We present a Patient Simulator that leverages real world patient encounters which cover a broad range of conditions and symptoms to prov...

Hybrid Ensemble of Segmentation-Assisted Classification and GBDT for Skin Cancer Detection with Engineered Metadata and Synthetic Lesions from ISIC 2024 Non-Dermoscopic 3D-TBP Images

Skin cancer is among the most prevalent and life-threatening diseases worldwide, with early detection being critical to patient outcomes. This work ...

On the Robustness of Tabular Foundation Models: Test-Time Attacks and In-Context Defenses

Recent tabular Foundational Models (FM) such as TabPFN and TabICL, leverage in-context learning to achieve strong performance without gradient updat...

A Dynamic Framework for Semantic Grouping of Common Data Elements (CDE) Using Embeddings and Clustering

This research aims to develop a dynamic and scalable framework to facilitate harmonization of Common Data Elements (CDEs) across heterogeneous biome...

A Large Language Model-Supported Threat Modeling Framework for Transportation Cyber-Physical Systems

Modern transportation systems rely on cyber-physical systems (CPS), where cyber systems interact seamlessly with physical systems like transportatio...

On the use of natural language processing to implement the target trial framework using unstructured data from the electronic health record.

The increasing availability and accessibility of electronic health record (EHR) data has made it a rich secondary source to conduct comparative effect...

Multi-Gate Mixture of Multi-View Graph Contrastive Learning on Electronic Health Record.

Electronic Health Record (EHR) is the digital form of patient visits that contains various medical data, including diagnosis, treatment, and lab event...

Semi-Supervised PARAFAC2 Decomposition for Computational Phenotyping Using Electronic Health Records.

Computational phenotyping uses data mining methods to extract clusters of clinical descriptors, known as phenotypes, from electronic health records (E...

Predicting postoperative chronic opioid use with fair machine learning models integrating multi-modal data sources: a demonstration of ethical machine learning in healthcare.

OBJECTIVE: Building upon our previous work on predicting chronic opioid use using electronic health records (EHR) and wearable data, this study levera...

Beyond Phecodes: leveraging PheMAP to identify patients lacking diagnosis codes in electronic health records.

OBJECTIVE: Diagnosis codes documented in electronic health records (EHR) are often relied upon to clinically phenotype patients for biomedical researc...

Development and External Validation of a Detection Model to Retrospectively Identify Patients With Acute Respiratory Distress Syndrome.

OBJECTIVE: The aim of this study was to develop and externally validate a machine-learning model that retrospectively identifies patients with acute r...