Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
Jul 6, 2024
OBJECTIVES: Machine learning methods have gained much attention in health sciences for predicting various health outcomes but are scarcely used in pharmacoepidemiology. The ability to identify predictors of suboptimal medication use is essential for ...
BACKGROUND: In binary classification for clinical studies, an imbalanced distribution of cases to classes and an extreme association level between the binary dependent variable and a subset of independent variables can create significant classificati...
The global prevalence of prediabetes is expected to reach 8.3% (587 million people) by 2045, with 70% of people with prediabetes developing diabetes during their lifetimes. We aimed to classify community-dwelling adults with a high risk for prediabet...
Scandinavian journal of primary health care
Jul 3, 2024
AIM: Machine learning techniques have demonstrated success in predictive modeling across various clinical cases. However, few studies have considered predicting the use of multisectoral health and social services among older adults. This research aim...
International journal of medical informatics
Jul 3, 2024
BACKGROUND: Real-world data with decades-long medical records are increasingly available alongside the growing adoption of machine learning in healthcare research. We evaluated the performance of machine learning models in predicting the risk of Alzh...
BACKGROUND: Early predictors of postoperative complications can risk-stratify patients undergoing colorectal cancer surgery. However, conventional regression models have limited power to identify complex nonlinear relationships among a large set of v...
IMPORTANCE: Sleep is critical to a person's physical and mental health and there is a need to create high performing machine learning models and critically understand how models rank covariates.
AIM: To develop a decision-support tool for predicting extubation failure (EF) in neonates with bronchopulmonary dysplasia (BPD) using a set of machine-learning algorithms.
PURPOSE: This study aims to develop and validate a prediction model for delirium in elderly ICU patients and help clinicians identify high-risk patients at the early stage.
Stroke is the leading cause of death and disability worldwide. Cadmium is a prevalent environmental toxicant that may contribute to cardiovascular disease, including stroke. We aimed to build an effective and interpretable machine learning (ML) model...
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