Information Technology

Machine Learning Models for Predicting Mortality Risk and Survival Time in Lung Cancer Patients Treated with EGFR-TKIs.

This study develops machine learning models to predict patient mortality and estimate survival time using electronic health record (EHR) data from three Taipei Medical University-affiliated hospitals (TMU Hospital, Wan-Fang Hospital, and Shuang Ho Hospital). We built and evaluated predictive models for (1) binary mortality risk and (2) time-to-event survival. In testing, the best classification mo...

Explainable Framework for Ontology-Based Similarity: A Use Case on SNOMED CT.

In healthcare, being able to efficiently manipulate and compare concepts in ontologies is crucial to enable semantic interoperability of clinical data. Most ontology-based similarity functions return a single score with little actionable justification, limiting their trust and use in clinical workflows. In this paper, we focus on the explainability of similarity computations rather than on definin...

Multimodal Cardiovascular Disease Detection Using ECG Image and EHR.

ECG is an important signal for cardiovascular disease prediction. Since the ECG signals are often stored as images in clinical practice, we transforme...

Conflicting Logics in EHR-AI Integration: Human Factors and Sociotechnical Tensions in Clinical Work.

Electronic health record (EHR) systems have evolved from static repositories of patient information into dynamic platforms that increasingly influence...

Determinants of the Acceptance of Health-Related Use of Artificial Intelligence in the General Population: A Cross-Sectional Study.

The successful integration of artificial intelligence (AI) in healthcare hinges on user acceptance, which is influenced by cognitive, emotional, and c...

Advances and Trends in Clinical Information Systems: From AI to Machine Learning Applications and Beyond.

Clinical Information systems (CIS) are a technological pillar of modern healthcare, enabling data driven decision-making, care coordination and patien...

Aligning AI-Native 6G Healthcare Systems with EU Ethical and Legal Frameworks.

The convergence of AI, robotics, and 6G networks is reshaping healthcare through real-time This paper applies a structured regulatory-technical analys...

Leveraging Large Language Models with Retrieval-Augmented Generation for Semantic Mapping of Clinical Data Lakes to SNOMED CT.

Mapping local clinical concepts to standardized terminologies such as SNOMED CT is essential for semantic interoperability and large-scale research, b...

Using Prompt Engineering to Optimize a RAG Pipeline for EHR-Nursing Data Standardization.

Standardizing nursing care plan data from electronic health records is critical for interoperability and large-scale research but is often hindered by...

Enhancing Ontology Engineering with Large Language Models: A Stage-Wise Human-in-the-Loop Study.

Ontology engineering plays a critical role in modelling structured knowledge and ensuring semantic interoperability in digital healthcare. However, ma...

Describing Data Processing in FHIR: AI-Assisted Interoperability for Cancer Stage Extraction.

INTRODUCTION: Creating interoperable clinical data models in FHIR is essential but often labor-intensive. This study explores the use of Generative AI...

Generative AI in Dutch General Practice: A Pilot Study of Current Use and Future Perspectives.

Dutch general practice is under increasing pressure from workforce shortages and administrative workload. Generative artificial intelligence (GenAI) i...

Transforming Annotated Clinical Narratives into Pruned Interoperable Knowledge Graphs with SNOMED CT.

INTRODUCTION: Clinical narratives are difficult to process due to unstructured text, abbreviations, and jargon, which limit semantic interoperability....

Towards Clinical NLP in a Low-Resource Language: Structuring Estonian Clinical Text.

Healthcare sectors generate large volumes of unstructured text, yet mature clinical NLP is lacking for low-resource languages. This project targets Es...

REDCap Ontology Annotation Made Easy (ROME) - A REDCap Module for Simplified Metadata Element Annotation.

Semantic annotation of study metadata elements with concept codes from medical terminologies is essential in order to make biomedical data FAIR (Finda...

Ontology-Aligned Representation of Adverse Events in a Multiple Myeloma Clinical Trial.

To explore the potential of ontologies in improving adverse event identification, we developed ontology-aligned representations of adverse events extr...

Federated Deception Intelligence and the ASKOS Solution for Cyber-Resilient Healthcare Infrastructures.

Healthcare infrastructures face escalating cybersecurity risks caused by legacy systems, connected IoMT devices, and complex data-sharing environments...

Navigating the "Valley of Death": An Open-Source, Modular Framework for Generative AI in Healthcare.

Generative AI (GenAI) adoption remains limited, particularly in under-resourced settings, despite its many applications. This disparity arises from fr...

Enabling Privacy-Preserving Federated Learning in Healthcare: The FLAME Architecture and Policy Framework.

Federated Learning enables collaborative AI development in healthcare without sharing patient data, addressing privacy and regulatory constraints like...

HeXEHRS: Design and Implementation of a FHIR-Based Cloud EHR and Client for Depopulated Regions with AI and Digital-Twin Integration.

Japan's national initiative for scientific infrastructure emphasizes equitable healthcare delivery in depopulated regions facing rapid aging and physi...