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Does Whole Brain Radiomics on Multimodal Neuroimaging Make Sense in Neuro-Oncology? A Proof of Concept Study.

Studies in health technology and informatics
Employing a whole-brain (WB) mask as a region of interest for extracting radiomic features is a feasible, albeit less common, approach in neuro-oncology research. This study aims to evaluate the relationship between WB radiomic features, derived from...

GRU-D Characterizes Age-Specific Temporal Missingness in MIMIC-IV.

Studies in health technology and informatics
Temporal missingness, defined as unobserved patterns in time series, and its predictive potentials represent an emerging area in clinical machine learning. We trained a gated recurrent unit with decay mechanisms, called GRU-D, for a binary classifica...

An Interpretable Model for Predicting Acute Myocardial Infarction in Distinct Patient Profiles.

Studies in health technology and informatics
INTRODUCTION: Acute myocardial infarction (AMI) is highly prevalent (3.8% in developed countries), affecting heterogenous populations, and can be influenced by varied factors, including demographics, clinical risk factors, and comorbidities. Identify...

Examining Physicians' Intentions to Use AI: The Roles of Accountability, Necessity, and Usefulness.

Studies in health technology and informatics
This study explores the under-researched area of how perceived necessity and accountability influence physicians' intention to use AI in healthcare. Conducted across three general hospitals in Taiwan, the research analyzed 398 valid responses from ph...

Co-Designing a "win-win" in Predictive AI: First Results from Interviews and Focus Groups with Persons with Parkinson's Disease.

Studies in health technology and informatics
This study explored the perspectives of people with Parkinson's disease (PwP) involved in the co-design of AI tools for PD care. The aim was to understand PwP perspectives on AI tools and identify factors influencing their engagement. A qualitative t...

Predicting 1-Year Survival Using Machine Learning in Very Old Patients Before ICU Admission.

Studies in health technology and informatics
Discussions about the benefits of admitting very old individuals to intensive care unit (ICU) remain challenging. We hypothesized that data-driven algorithms could leverage extensive real-life data to provide more accurate long-term predictions. Our ...

Bias Detection in Histology Images Using Explainable AI and Image Darkness Assessment.

Studies in health technology and informatics
The study underscores the importance of addressing biases in medical AI models to improve fairness, generalizability, and clinical utility. In this paper, we present a novel framework that combines Explainable AI (XAI) with image darkness assessment ...

Machine Learning Models Predicting Hospital Admissions During Chemotherapy Utilising Longitudinal Symptom Severity Reports and Patient-Reported Outcome Measures.

Studies in health technology and informatics
Chemotherapy toxicity can lead to acute hospital admissions, negatively impacting the healthcare system and patients' well-being. Machine learning (ML) models identifying patients at risk of emergency admissions are often developed on data lacking pa...

Using Optimal Survival Tree Model for AF Event-Free Survival Time Prediction.

Studies in health technology and informatics
This study presents a methodology to acquire, integrate, and analyze clinical data based on an innovative application of the Optimal Survival Tree (OST) algorithm. It has been tested on a clinical dataset of 4114 patients with a follow-up of 59.0 ± 1...