IMPORTANCE: The diagnosis of schizophrenia and bipolar disorder is often delayed several years despite illness typically emerging in late adolescence or early adulthood, which impedes initiation of targeted treatment.
Purpose To develop and evaluate machine learning and deep learning-based models for automated protocoling of emergency brain MRI scans based on clinical referral text. Materials and Methods In this single-institution, retrospective study of 1953 emer...
Journal of cataract and refractive surgery
May 1, 2025
PURPOSE: To develop a prediction model based on machine learning to calculate the postoperative vault and the ideal implantable collamer lens (ICL) size, considering for the first time the implantation orientation in a White population.
Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
May 1, 2025
STUDY OBJECTIVES: Undiagnosed or untreated moderate-to-severe obstructive sleep apnea (OSA) increases cardiovascular risks and mortality. Early and efficient detection is critical, given its high prevalence. We aimed to develop a practical and effici...
Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
May 1, 2025
STUDY OBJECTIVES: Continuous positive airway pressure (CPAP) is the treatment of choice for obstructive sleep apnea; however, some people have residual respiratory events or require significantly higher CPAP pressure while on therapy. Our objective w...
The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry
May 1, 2025
BACKGROUND: Primary healthcare institutions find identifying individuals with dementia particularly challenging. This study aimed to develop machine learning models for identifying predictive features of older adults with normal cognition to develop ...
European eating disorders review : the journal of the Eating Disorders Association
May 1, 2025
OBJECTIVE: Machine learning (ML) techniques have shown promise for enhancing prediction of clinical outcomes; however, its application to predicting binge eating has been scarcely explored. We applied ML techniques to predict binge eating onset (vs. ...
BACKGROUND & AIMS: Enhanced computed tomography (CT) is the primary method for focal liver lesion diagnosis. We aimed to use automated machine learning (AutoML) algorithms to differentiate between benign and malignant focal liver lesions on the basis...
OBJECTIVE: This study aims to employ machine learning (ML) tools to cluster patients hospitalized for acute exacerbations of chronic obstructive pulmonary disease (COPD) based on their diverse social and clinical characteristics. This clustering is i...
PURPOSE: Bone metastasis is a critical complication in prostate cancer, significantly impacting patient prognosis and quality of life. This study aims to enhance bone metastasis prediction using machine learning (ML) techniques by integrating dynamic...
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