This paper describes a supervised machine learning approach for identifying heart disease risk factors in clinical text, and assessing the impact of annotation granularity and quality on the system's ability to recognize these risk factors. We utiliz...
De-identification, identifying and removing all protected health information (PHI) present in clinical data including electronic medical records (EMRs), is a critical step in making clinical data publicly available. The 2014 i2b2 (Center of Informati...
The 2014 i2b2/UTHealth natural language processing shared task featured a track focused on identifying risk factors for heart disease (specifically, Cardiac Artery Disease) in clinical narratives. For this track, we used a "light" annotation paradigm...
IEEE journal of biomedical and health informatics
Jul 30, 2014
Machine learning is being used in a wide range of application domains to discover patterns in large datasets. Increasingly, the results of machine learning drive critical decisions in applications related to healthcare and biomedicine. Such health-re...
Australian health review : a publication of the Australian Hospital Association
Dec 4, 2025
The integration of artificial intelligence (AI) into Australian healthcare promises to improve diagnostic accuracy, workflow efficiency, and personalised care, yet it also introduces critical cybersecurity vulnerabilities that threaten not only data ...
Artificial intelligence (AI) is rapidly transforming radiology, with applications spanning disease detection, lesion segmentation, workflow optimization, and report generation. As these tools become more integrated into clinical practice, new concern...
IEEE transactions on visualization and computer graphics
Oct 1, 2025
Large Language Models (LLMs) have gained significant attention but also raised concerns due to the risk of misuse. Jailbreak prompts, a popular type of adversarial attack towards LLMs, have appeared and constantly evolved to breach the safety protoco...
Studies in health technology and informatics
Sep 3, 2025
INTRODUCTION: Machine learning (ML) and deep learning (DL) models in healthcare traditionally rely on server-centric architectures, where sensitive patient data is transmitted to external servers for processing via frameworks like Flask, raising sign...
The availability of health technologies has facilitated improvements in the quality of care, playing a vital role in both hospital environments and remote patient monitoring. However, the growing complexity of these technologies has also led to an in...
With the rise Artificial Intelligence (AI), mitigation strategies may be needed to integrate AI-enabled medical software responsibly, ensuring ethical alignment and patient safety. This study examines how to mitigate the key risks identified by the E...
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