BACKGROUND: Currently available cardiovascular disease (CVD) risk prediction tools may underestimate the risk in individuals with schizophrenia. OBJECTIVE: To develop and externally validate 5-year CVD risk prediction models for people with schizophr...
BACKGROUND: Trust in artificial intelligence (AI) remains a critical barrier to the adoption of AI in mental health care. This study explores the formation of trust in an AI mental health model and its human-computer interface among clinicians at a w...
OBJECTIVES: Postoperative complications (PCs) require substantial resources to manage and are cumbersome to monitor. Artificial intelligence (AI), particularly natural language processing (NLP), offers a potential solution by automating and streamlin...
UNLABELLED: This study used explainable AI to improve the Danish FREM model for predicting one-year risk of major osteoporotic fractures in over 2.4 million individuals aged ≥ 45. A DART boosting algorithm improved performance (AUC 0.77), with explai...
Understanding species-level abundance dynamics in complex microbial communities is key to managing microbial ecosystems, yet it remains a major challenge. In wastewater treatment plants (WWTPs), the presence and abundance of process-critical bacteria...
Sleep problems among young adults pose a major public health challenge. Leveraging nationwide health surveys and registers from Denmark, we investigated patterns of sleep problems from late adolescence to adulthood and explored early life-course dete...
BACKGROUND: Dyspnea is a common cause of hospitalization, posing diagnostic challenges among older adult patients with multimorbid conditions. Chest computed tomography (CT) scans are increasingly used in patients with dyspnea and offer superior diag...
Colonoscopy is the leading endoscopic technique when it comes to implementing artificial intelligence-based tools to optimize the procedure. However, no database consisting of the colonoscope's coordinates exists, allowing for a mapping with timestam...
BACKGROUND AND PURPOSE: We aimed to externally validate machine learning models developed in Norway by evaluating their predictive outcome of disability and pain 12 months after lumbar disc herniation surgery in a Swedish and Danish cohort.
Early warning scores are used to assess acute patients' risk of being in a critical situation, allowing for early appropriate treatment, avoiding critical outcomes. The early warning scores use changes in vital signs to provide an assessment, however...
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