Enhanced meta ensemble stacking approach with XGBoost and optuna based detection of Parkinson’s disease

Parkinson’s disease (PD), a progressive neurological disorder affecting motor function, has been significantly rising in prevalence in recent years. Current diagnostic methods, relying on clinical observations, neurological exams, and periodical DaTscan imaging, may exhibit reduced sensitivity in the early stages. To develop a robust and multimodal machine learning model for early detection, an Ensemble Approach […]

Advancing the adoption of oncology decision support tools in Europe: insights from CAN.HEAL

Effective cancer care increasingly depends on digital decision support tools (DSTs) to interpret complex clinical, molecular, and genomic data and guide personalised treatment decisions. However, the oncology DST (oncDST) landscape remains fragmented, with limited interoperability, inconsistent standards, and uneven clinical adoption across healthcare systems. This fragmentation hinders routine clinical use and impedes the demonstration of […]

Early Type 2 diabetes risk prediction using explainable machine learning in a two-stage approach

BackgroundDiabetes is a chronic disease characterized by elevated blood glucose levels. Without early detection and proper management, it can lead to serious complications and increase healthcare costs. Its global prevalence is rising, with many cases remaining undiagnosed. In this study, we developed an explainable machine learning model using a two-stage approach for predicting diabetes.MethodsFive machine […]

Ethical examination of AI coaches: privacy, bias, and responsibility

The integration of artificial intelligence (AI) into sports, particularly through AI-driven coaching systems, marks a transformative advancement with the potential to revolutionize personalized training. AI coaches can create customized, data-driven training programs designed to optimize athletic performance. However, this technological progress also brings with it significant ethical concerns, including privacy violations, data biases, and ambiguous […]

Cybersecurity breaches in medical devices: analyzing FDA safety communications in response to patient security concerns

IntroductionThe increasing integration of connected medical devices and internet of things (IoT) technologies in healthcare has significantly improved patient care and operational efficiency. However, this rapid digital transformation has also introduced serious cybersecurity vulnerabilities in medical devices, posing risks to patient safety and sensitive health data. Cybersecurity threats can allow unauthorized remote access to devices, […]

Depression subtype classification from social media posts: few-shot prompting vs. fine-tuning of large language models

BackgroundSocial media provides timely proxy signals of mental health, but reliable tweet-level classification of depression subtypes remains challenging due to short, noisy text, overlapping symptomatology, and labeling bias. Large language models (LLMs) are increasingly used in mental health for tasks such as symptom extraction, risk screening, and triage, yet their reliability for fine-grained depression subtype […]

An in-home engagement and usability study of GeRI: an open-source platform for remote symptom assessment and wearable activity monitoring in men with prostate cancer

Geriatric assessment (GA) is underused in oncology because clinic-based implementation is time- and resource-intensive, limiting routine evaluation of frailty and treatment tolerance. Existing digital tools often rely on proprietary devices and closed analytic pipelines. We developed the Geriatric Remote Initiative (GeRI), an open-source platform integrating a wrist-worn accelerometer, smart scale, and tablet interface with reproducible […]

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