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Predictive Analysis of Amyotrophic Lateral Sclerosis Progression and Mortality in a Clinic Cohort From Singapore.
Muscle & nerve
Apr 23, 2025
INTRODUCTION: There is currently no comprehensive Amyotrophic Lateral Sclerosis (ALS) patient database in Singapore comparable to those available in Europe and the United States. We established the Singapore ALS registry (SingALS) to draw meaningful ...
Feasibility of human ethomic biomarkers for the diagnosis and monitoring of hip osteoarthritis.
Journal of biomechanics
Apr 23, 2025
Radiographic imaging is typically used to diagnose osteoarthritis (OA). However, patients would typically be sent for imaging after they present to a physician because of joint pain. By this time, the condition is likely irreversible. This study aims...
Deep learning-based post hoc denoising for 3D volume-rendered cardiac CT in mitral valve prolapse.
The international journal of cardiovascular imaging
Apr 23, 2025
We hypothesized that deep learning-based post hoc denoising could improve the quality of cardiac CT for the 3D volume-rendered (VR) imaging of mitral valve (MV) prolapse. We aimed to evaluate the quality of denoised 3D VR images for visualizing MV pr...
The predictive role of identifying frailty in assessing the need for palliative care in the elderly: the application of machine learning algorithm.
Journal of health, population, and nutrition
Apr 23, 2025
BACKGROUND: Palliative care is a key component of integrated care to improve care quality and reduce hospitalization costs for patients with chronic obstructive pulmonary disease (COPD). This study aims to use machine learning algorithms to create an...
An integrated machine learning model of transcriptomic genes in multi-center chronic obstructive pulmonary disease reveals the causal role of TIMP4 in airway epithelial cell.
Respiratory research
Apr 23, 2025
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a heterogeneous syndrome, resulting in inconsistent findings across studies. Identifying a core set of genes consistently involved in COPD pathogenesis, independent of patient variability, i...
Interpretable machine learning model for predicting delirium in patients with sepsis: a study based on the MIMIC data.
BMC infectious diseases
Apr 23, 2025
OBJECTIVE: The aim of this study was to construct interpretable machine learning models to predict the risk of developing delirium in patients with sepsis and to explore the impact of delirium on the 28-day survival rate of patients.
Vascular-related biological stress, DNA methylation, allostatic load and domain-specific cognition: an integrated machine learning and causal inference approach.
BMC neurology
Apr 23, 2025
BACKGROUND: Vascular disease in aging populations spans a wide range of disorders including strokes, circulation disorders and hypertension. As individuals age, vascular disorders co-occur and hence exert combined effects. In the present study we int...
Elucidating predictors of preoperative acute heart failure in older people with hip fractures through machine learning and SHAP analysis: a retrospective cohort study.
BMC geriatrics
Apr 23, 2025
BACKGROUND: Acute heart failure (AHF) has become a significant challenge in older people with hip fractures. Timely identification and assessment of preoperative AHF have become key factors in reducing surgical risks and improving outcomes.
Comparison of machine learning models with conventional statistical methods for prediction of percutaneous coronary intervention outcomes: a systematic review and meta-analysis.
BMC cardiovascular disorders
Apr 23, 2025
INTRODUCTION: Percutaneous coronary intervention (PCI) has been the main treatment of coronary artery disease (CAD). In this review, we aimed to compare the performance of machine learning (ML) vs. logistic regression (LR) models in predicting differ...
Development and validation of a machine learning risk prediction model for asthma attacks in adults in primary care.
NPJ primary care respiratory medicine
Apr 23, 2025
Primary care consultations provide an opportunity for patients and clinicians to assess asthma attack risk. Using a data-driven risk prediction tool with routinely collected health records may be an efficient way to aid promotion of effective self-ma...
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