Latest AI and machine learning research in information technology for healthcare professionals.
The rapid evolution of deepfake technology, driven by deep learning and generative models such as Generative Adversarial Networks, has revolutionized digital content creation while simultaneously introducing serious threats to privacy, security, and digital trust. This study addresses the increasing risks posed by deepfakes, synthetically generated images, audio, and text, by developing a comprehe...
BACKGROUND: Research is needed to develop more accurate readmission prediction models that identify patients at the highest risk of readmission after their initial pneumonia hospitalization. Improving prediction accuracy will support the implementation of more effective, personalized interventions to lower readmission rates. Published models tend to rely on traditional methods or advanced machine ...
Medical imaging data are an essential resource for research and teaching; however, regulations such as the Health Insurance Portability and Accountabi...
Early identification of ICU patients at high mortality risk is essential for triage and timely intervention. We present adaptive layer fusion with int...
Longitudinal electronic health record (EHR) trajectories are highly heterogeneous, sparse, and irregular, making unsupervised temporal pattern discove...
The biobank is a functional unit that facilitates and improves research by storing biological samples and associated data. As such, it is a key resour...
BACKGROUND: In an attempt to overcome the space-time limitations of traditional training we used a new telemedicine home-training model (Videotraining...
The increasing digitalization of healthcare necessitates laboratory data interoperability to ensure reliable clinical decision-making, efficient data ...
OBJECTIVES: To operationalize and temporally validate an electronic medical record (EMR)-integrated machine learning system (Big data-driven Evaluatio...
Heart failure management in skilled nursing facilities (SNFs) is complicated by limited access to specialists, incomplete clinical documentation, and ...
In a context of increasing digitalisation within the wind energy sector, the industry is facing a growing need for professionals with advanced digital...
OBJECTIVES: Although advancements in electronic health records (EHRs) have improved clinical productivity, digital administrative responsibilities hav...
Regulatory authorities worldwide are developing strategies to integrate artificial intelligence (AI) into the lifecycle of health products, technologi...
Cardiovascular disease is the leading cause of death worldwide, with coronary artery disease the most prevalent cause. Although artificial intelligenc...
PURPOSE: To validate the performance of an AI system (TRIAGE) for cancer trial eligibility screening using real-world longitudinal electronic health r...
OBJECTIVE: To improve in-hospital mortality prediction from longitudinal electronic health records (EHRs) by extracting mortality-related high-risk cl...
BACKGROUND: Artificial intelligence (AI)-enabled "ambient" documentation may reduce clinician administrative burdens and improve care delivery, but im...
Recurrent acute care visits are a common yet preventable outcome for many children with asthma. Machine learning (ML) applied to electronic medical re...
PURPOSE: Foundation models pretrained on structured electronic health record (EHR) data promise improved predictive performance, sample efficiency and...
BACKGROUND: Background Clinical documentation is a major contributor to clinician workload and burnout, with physicians spending more than half of the...