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Comparative ranking of marginal confounding impact of natural language processing-derived versus structured features in pharmacoepidemiology.
Computers in biology and medicine
Feb 17, 2025
OBJECTIVE: To explore the ability of natural language processing (NLP) methods to identify confounder information beyond what can be identified using claims codes alone for pharmacoepidemiology.
Supervised machine learning compared to large language models for identifying functional seizures from medical records.
Epilepsia
Feb 17, 2025
OBJECTIVE: The Functional Seizures Likelihood Score (FSLS) is a supervised machine learning-based diagnostic score that was developed to differentiate functional seizures (FS) from epileptic seizures (ES). In contrast to this targeted approach, large...
Bridging Neuroscience and Machine Learning: A Gender-Based Electroencephalogram Framework for Guilt Emotion Identification.
Sensors (Basel, Switzerland)
Feb 17, 2025
This study explores the link between the emotion "guilt" and human EEG data, and investigates the influence of gender differences on the expression of guilt and neutral emotions in response to visual stimuli. Additionally, the stimuli used in the stu...
Robot-Assisted Approach to Diabetes Care Consultations: Enhancing Patient Engagement and Identifying Therapeutic Issues.
Medicina (Kaunas, Lithuania)
Feb 17, 2025
: Diabetes is a rapidly increasing global health challenge compounded by a critical shortage of diabetes care and education specialists. Robot-assisted diabetes care offers a cost-effective and scalable alternative to traditional methods such as trai...
Identification of 17 novel epigenetic biomarkers associated with anxiety disorders using differential methylation analysis followed by machine learning-based validation.
Clinical epigenetics
Feb 17, 2025
BACKGROUND: The changes in DNA methylation patterns may reflect both physical and mental well-being, the latter being a relatively unexplored avenue in terms of clinical utility for psychiatric disorders. In this study, our objective was to identify ...
Prediction of depressive disorder using machine learning approaches: findings from the NHANES.
BMC medical informatics and decision making
Feb 17, 2025
BACKGROUND: Depressive disorder, particularly major depressive disorder (MDD), significantly impact individuals and society. Traditional analysis methods often suffer from subjectivity and may not capture complex, non-linear relationships between ris...
Comparative analysis of intestinal tumor segmentation in PET CT scans using organ based and whole body deep learning.
BMC medical imaging
Feb 17, 2025
BACKGROUND: 18-Fluoro-deoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) is a valuable imaging tool widely used in the management of cancer patients. Deep learning models excel at segmenting highly metabolic tumors but face ch...
An effective multi-step feature selection framework for clinical outcome prediction using electronic medical records.
BMC medical informatics and decision making
Feb 17, 2025
BACKGROUND: Identifying key variables is essential for developing clinical outcome prediction models based on high-dimensional electronic medical records (EMR). However, despite the abundance of feature selection (FS) methods available, challenges re...
A predictive model for recurrence in patients with borderline ovarian tumor based on neural multi-task logistic regression.
BMC cancer
Feb 17, 2025
BACKGROUND: Effective management of patients with borderline ovarian tumor (BOT) requires the timely identification of those at a higher risk of recurrence. Artificial neural networks have been successfully used in many areas of clinical event predic...
Interpretable machine learning approaches for children's ADHD detection using clinical assessment data: an online web application deployment.
BMC psychiatry
Feb 17, 2025
BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a prevalent mental disorder characterized by hyperactivity, impulsivity, and inattention. This study aims to develop a verifiable and interpretable machine learning model to identify ADHD...
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