Information Technology

PENet+: A Lightweight Residual Transformer Framework for Efficient Image Steganalysis

Image steganalysis, the detection of hidden information embedded in digital images, is a core component of modern cybersecurity and digital forensics. Recent residual Transformer architectures, such as the Pixel-Difference-Convolution and Enhanced-Transformer-Network (PENet) [1], achieve strong detection accuracy, but their computational and memory demands hinder deployment in resource-constrained...

A Comparison of Manual and Automated Approaches to Developing Computable Algorithms for Identifying Acute Pancreatitis

Objective: Clinical phenotyping methods that rely on clinical and informatics expertise can be time-intensive and costly. We tested both manual and highly automated approaches using electronic health record (EHR) data to identify an FDA Sentinel Initiative health outcome of interest, acute pancreatitis. Materials and Methods: We trained and evaluated machine learning algorithms using EHR data with...

Characterizing artificial intelligence (AI) psychosis in a large academic medical setting: evidence of the new clinical phenomenon and the vulnerability of those in early phases of psychosis

Background: Concerns about "AI psychosis" have swirled in the media since ChatGPT's release, but few systematic analyses exist. We therefore conducted...

Beyond Injection Detection: A Positive-Security Prompt Firewall that Closes the Scope and PHI Gap SOTA Classifiers Miss in Healthcare

Large language models embedded in autonomous agents process trusted instructions and untrusted data in one context window, leaving them open to direct...

FDM: A Framework for Decision-making to build ML-based Malware detection systems

Selecting appropriate machine learning (ML) configurations for malware detection is a complex, multi-criteria problem. Model choice, feature engineeri...

Accelerating Reproducible Research in Synthetic EHR Generation

The generation of high-fidelity synthetic Electronic Health Records (EHR) is crucial for advancing medical research while preserving patient privacy. ...

Explainable AI-Driven Cyber Risk Analytics and Model Reliability Assessment for Intelligent Governance of U.S. Critical Infrastructure: An XGBoost and SHAP-Based Intrusion Detection Framework

The increasing penetrations of the critical infrastructure sector in the United States with intelligent digital technologies have greatly increased ex...

Hybrid CNN-LSTM Framework for Intelligent Cyber Attack Detection and Prevention in U.S. Critical Digital Infrastructure: A Comparative Machine Learning Evaluation on CSE-CIC-IDS2018

Digital infrastructure is growing at a rapid pace in the United States, and as a result, exposure to advanced cyber threats to critical sectors includ...

Predicting the timing of first sustained cognitive worsening in Alzheimer's disease using real-world clinical data and machine learning

Background: Cognitive assessments are sparsely documented in electronic health records (EHRs), limiting scalable detection of cognitive worsening in r...

Augmenting Structured Diagnoses through Effective Use of Pre-trained Large Language Models on Clinical Notes

Objective Clinical narrative provides a unique window into provider reasoning and attribution, but use has been limited by resource requirements and e...

Stigmatizing Language Detection in Opioid Use Disorder Patient-Directed Discharge Clinical Documentation: A Privacy-Preserving Analysis Using a Locally Deployed Large Language Model

Objective: Stigmatizing language in the electronic health record (EHR) has been associated with adverse patient experience in substance use disorder c...

TaxonMatch: taxonomic integration and tree construction from heterogeneous biological databases

Integrating taxonomic data across heterogeneous biological databases remains a major challenge in biodiversity research due to non-standardized nomenc...

Generation and Evaluation of Realistic Synthetic Clinical Progress Notes for Prostate Cancer using Large Language Models.

Background and Objective: Access to real-world electronic health records (EHRs) remains limited by privacy, governance and annotation constraints, hin...

Learning Dynamic Graph Representations through Timespan View Contrasts

The rich information underlying graphs has inspired further investigation of unsupervised graph representation. Existing studies mainly depend on node...

Prospective evaluation of multimodal respiratory failure prediction: Do chest X-rays improve performance beyond EHR signals?

Early prediction of respiratory failure is critical for timely clinical intervention in intensive care units. Existing electronic health record (EHR)-...

Extraction of Human Phenotype Ontology (HPO) Concepts from Clinical Notes Utilizing Large Language Models (LLM) with Model Context Protocol (MCP)

Background: Accurate extraction of Human Phenotype Ontology (HPO) terms from clinical notes is essential for variant prioritization and genetic diagno...

Machine Learning Estimation of Gestational Age at Delivery Using Linked Mother-Infant Electronic Health Records Across Two Health Systems

Objective This study aimed to train and evaluate supervised machine learning algorithms using electronic health record (EHR) data to accurately estima...

Design and Validation of an AI-Assisted Sequential Screening Framework for Psychological Distress in Glaucoma

Purpose: Psychological distress is highly prevalent in glaucoma and is associated with worse adherence, reduced quality of life, and faster disease pr...

Evaluating Large Language Models for Translating Multimodal Phenotype Documentations into Executable EHR Phenotyping Algorithms

Research applications of electronic health record (EHR) phenotypes require translating clinical definitions into executable EHR database queries, a la...

ChronoMedicalWorld: A Medical World Model for Learning Patient Trajectories from Longitudinal Care Data

Long-horizon clinical simulation -- predicting how a patient's physiology evolves over years under specified interventions -- is central to chronic-di...