Information Technology

Generating patient cohorts from electronic health records using two-step retrieval-augmented text-to-SQL generation

Clinical cohort definition is crucial for patient recruitment and observational studies, yet translating inclusion/exclusion criteria into SQL queries remains challenging and manual. We present an automated system utilizing large language models that combines criteria parsing, two-level retrieval augmented generation with specialized knowledge bases, medical concept standardization, and SQL gene...

Generating Clinically Realistic EHR Data via a Hierarchy- and Semantics-Guided Transformer

Generating realistic synthetic electronic health records (EHRs) holds tremendous promise for accelerating healthcare research, facilitating AI model development and enhancing patient privacy. However, existing generative methods typically treat EHRs as flat sequences of discrete medical codes. This approach overlooks two critical aspects: the inherent hierarchical organization of clinical coding...

FedMentalCare: Towards Privacy-Preserving Fine-Tuned LLMs to Analyze Mental Health Status Using Federated Learning Framework

With the increasing prevalence of mental health conditions worldwide, AI-powered chatbots and conversational agents have emerged as accessible tools...

The Illusion of Rights based AI Regulation

Whether and how to regulate AI is one of the defining questions of our times - a question that is being debated locally, nationally, and internation...

A Residual Multi-task Network for Joint Classification and Regression in Medical Imaging

Detection and classification of pulmonary nodules is a challenge in medical image analysis due to the variety of shapes and sizes of nodules and the...

Revealing Treatment Non-Adherence Bias in Clinical Machine Learning Using Large Language Models

Machine learning systems trained on electronic health records (EHRs) increasingly guide treatment decisions, but their reliability depends on the cr...

Towards softerware: Enabling personalization of interactive data representations for users with disabilities

Accessible design for some may still produce barriers for others. This tension, called access friction, creates challenges for both designers and en...

A Radon-Nikodým Perspective on Anomaly Detection: Theory and Implications

Which principle underpins the design of an effective anomaly detection loss function? The answer lies in the concept of Radon-Nikod\'ym theorem, a f...

Vision Language Models in Medicine

With the advent of Vision-Language Models (VLMs), medical artificial intelligence (AI) has experienced significant technological progress and paradi...

To Patch or Not to Patch: Motivations, Challenges, and Implications for Cybersecurity

As technology has become more embedded into our society, the security of modern-day systems is paramount. One topic which is constantly under discus...

Large Language Models are Powerful Electronic Health Record Encoders

Electronic Health Records (EHRs) offer considerable potential for clinical prediction, but their complexity and heterogeneity present significant ch...

Rebalancing the Scales: A Systematic Mapping Study of Generative Adversarial Networks (GANs) in Addressing Data Imbalance

Machine learning algorithms are used in diverse domains, many of which face significant challenges due to data imbalance. Studies have explored vari...

Partial and Fully Homomorphic Matching of IP Addresses Against Blacklists for Threat Analysis

In many areas of cybersecurity, we require access to Personally Identifiable Information (PII), such as names, postal addresses and email addresses....

Enhancing LLMs for Identifying and Prioritizing Important Medical Jargons from Electronic Health Record Notes Utilizing Data Augmentation

OpenNotes enables patients to access EHR notes, but medical jargon can hinder comprehension. To improve understanding, we evaluated closed- and open...

CVE-LLM : Ontology-Assisted Automatic Vulnerability Evaluation Using Large Language Models

The National Vulnerability Database (NVD) publishes over a thousand new vulnerabilities monthly, with a projected 25 percent increase in 2024, highl...

Enhancing Domain-Specific Retrieval-Augmented Generation: Synthetic Data Generation and Evaluation using Reasoning Models

Retrieval-Augmented Generation (RAG) systems face significant performance gaps when applied to technical domains requiring precise information extra...

From FAIR to CURE: Guidelines for Computational Models of Biological Systems

Guidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, ...

Integrating Generative AI in Cybersecurity Education: Case Study Insights on Pedagogical Strategies, Critical Thinking, and Responsible AI Use

The rapid advancement of Generative Artificial Intelligence (GenAI) has introduced new opportunities for transforming higher education, particularly...

Bibliometric Analysis of Scientific Production on the COVID-19 Effect in Information Sciences

This paper analyzes the scientific production on the COVID-19 effect in the area of Information Sciences from a bibliometric perspective. The object...

LabTOP: A Unified Model for Lab Test Outcome Prediction on Electronic Health Records

Lab tests are fundamental for diagnosing diseases and monitoring patient conditions. However, frequent testing can be burdensome for patients, and t...