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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.
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...
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...
Prediction of tuberculosis treatment outcomes using biochemical makers with machine learning.
BMC infectious diseases
Feb 17, 2025
BACKGROUND: Tuberculosis (TB) continues to pose a significant threat to global public health. Enhancing patient prognosis is essential for alleviating the disease burden.
Research on the development of an intelligent prediction model for blood pressure variability during hemodialysis.
BMC nephrology
Feb 17, 2025
OBJECTIVE: Blood pressure fluctuations during dialysis, including intradialytic hypotension (IDH) and intradialytic hypertension (IDHTN), are common complications among patients undergoing maintenance hemodialysis. Early prediction of IDH and IDHTN c...
A recursive embedding and clustering technique for unraveling asymptomatic kidney disease using laboratory data and machine learning.
Scientific reports
Feb 17, 2025
Traditional methods for diagnosing chronic kidney disease (CKD) via laboratory data may not be capable of identifying early kidney disease. Kidney biopsy is unsuitable for regular screening, and imaging tests are costly and time-consuming. Several st...
Multicenter study on predicting postoperative upper limb muscle strength improvement in cervical spinal cord injury patients using radiomics and deep learning.
Scientific reports
Feb 17, 2025
Cervical spinal cord injury is often catastrophic, frequently leading to irreversible impairment. MRI has become the gold standard for evaluating spinal cord injuries (SCI). Our study aimed to assess the accuracy of a radiomics approach, based on mac...
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