PURPOSES: Intermediate-risk prostate cancer (IR PCa) is the most common risk group for localized prostate cancer. This study aimed to develop a machine learning (ML) model that utilizes biopsy predictors to estimate the probability of IR PCa and asse...
BACKGROUND: Irritable Bowel Syndrome (IBS) is a prevalent condition characterized by dysregulated brain-gut interactions. Despite its widespread impact, the brain mechanism of IBS remains incompletely understood, and there is a lack of objective diag...
Borderline personality disorder (BPD) is associated with a high risk of suicide. Despite several risk factors being known, identifying vulnerable patients in clinical practice remains a challenge so far. The current study aimed at predicting suicide ...
Anxiety symptoms are among the most prevalent mental health disorders in adolescents, highlighting the need for scalable and accessible interventions. As anxiety often co-occurs with perceived stress during adolescence, stress interventions may offer...
International journal of medical informatics
Jan 3, 2025
BACKGROUND: One of the main challenges in the maintenance of registries is to keep a high follow-up rate and a reliable strategy to limit dropout is currently lacking. Aim of this study was to utilize machine learning (ML) models to highlight the cha...
BACKGROUND: The Adelaide Score is an artificial intelligence system that integrates objective vital signs and laboratory tests to predict likelihood of hospital discharge.
BACKGROUND: Specific pelvic bone dimensions have been identified as predictors of total mesorectal excision (TME) difficulty and outcomes. However, manual measurement of these dimensions (pelvimetry) is labor intensive and thus, anatomic criteria are...
BACKGROUND: Early screening for autism spectrum disorder (ASD) is crucial, yet current assessment tools in Chinese primary child care are limited in efficacy.
PURPOSE: Epidemiologic studies are important in assessing risk factors of mortality. Machine learning (ML) is efficient in analyzing multidimensional data to unravel dependencies between risk factors and health outcomes.
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