BACKGROUND: Heart failure (HF) is a public health concern with a wider impact on quality of life and cost of care. One of the major challenges in HF is the higher rate of unplanned readmissions and suboptimal performance of models to predict the read...
Heart failure (HF) is considered a global pandemic because of increasing prevalence, high mortality rate, frequent hospitalization, and associated economic burden. This study explores a noninvasive method that may help in managing HF patients by pred...
Heart failure (HF) is a major global health burden, and complex comorbidity patterns can worsen clinical outcomes and complicate patient care. This study aimed to identify distinct comorbidity-based clusters among HF patients and evaluate their assoc...
BACKGROUND: Stroke remains a global health challenge with high rates of mortality and rehospitalization placing significant demands on healthcare systems. Identifying factors that determine outcomes of post-hospitalization improves resource allocatio...
BACKGROUND: Cirrhosis is a leading cause of noncancer deaths in gastrointestinal diseases, resulting in high hospitalization and readmission rates. Early identification of high-risk patients is vital for proactive interventions and improving health c...
Hospital readmissions prolong patient suffering and increase healthcare expenditures. While several studies have attempted to develop prediction models to reduce readmissions, most have demonstrated modest predictive accuracy. To improve upon prior a...
Hospital readmission prediction is a crucial area of research due to its impact on healthcare expenditure, patient care quality, and policy formulation. Accurate prediction of patient readmissions within 30 days post-discharge remains a considerable ...
INTRODUCTION: Few models have predicted readmission following open ventral hernia repair (VHR), and none have assessed fairness. Fairness evaluation assesses whether predictive performance is similar across demographic groups, ensuring that biases ar...
BACKGROUND: Machine Learning (ML) has been transformative in healthcare, enabling more precise diagnostics, personalised treatment regimens and enhanced patient care. In cardiology, ML plays a crucial role in risk prediction and patient stratificati...
This study aims to develop a robust and accurate model to forecast 30-day readmissions for patients with diabetes by leveraging machine learning techniques. Diabetes, being a chronic condition with complex care needs, often leads to frequent hospital...
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