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Decision Support Techniques

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Integrating Social Determinants of Health in Machine Learning-Driven Decision Support for Diabetes Case Management: Protocol for a Sequential Mixed Methods Study.

JMIR research protocols
BACKGROUND: The use of both clinical factors and social determinants of health (SDoH) in referral decision-making for case management may improve optimal use of resources and reduce outcome disparities among patients with diabetes.

Enhanced machine learning models for predicting one-year mortality in individuals suffering from type A aortic dissection.

The Journal of thoracic and cardiovascular surgery
OBJECTIVE: The study objective was to develop and validate an interpretable machine learning model to predict 1-year mortality in patients with type A aortic dissection, improving risk classification and aiding clinical decision-making.

Five steps in performing machine learning for binary outcomes.

The Journal of thoracic and cardiovascular surgery
BACKGROUND: The use of machine learning (ML) in cardiovascular and thoracic surgery is evolving rapidly. Maximizing the capabilities of ML can help improve patient risk stratification and clinical decision making, improve accuracy of predictions, and...

Predicting paediatric asthma exacerbations with machine learning: a systematic review with meta-analysis.

European respiratory review : an official journal of the European Respiratory Society
BACKGROUND: Asthma exacerbations in children pose a significant burden on healthcare systems and families. While traditional risk assessment tools exist, artificial intelligence (AI) offers the potential for enhanced prediction models.

Machine learning for predicting in-hospital mortality in elderly patients with heart failure combined with hypertension: a multicenter retrospective study.

Cardiovascular diabetology
BACKGROUND: Heart failure combined with hypertension is a major contributor for elderly patients (≥ 65 years) to in-hospital mortality. However, there are very few models to predict in-hospital mortality in such elderly patients. We aimed to develop ...

Machine Learning Model for Predicting Risk Factors of Prolonged Length of Hospital Stay in Patients with Aortic Dissection: a Retrospective Clinical Study.

Journal of cardiovascular translational research
The length of hospital stay (LOS) is crucial for assessing medical service quality. This study aimed to develop machine learning models for predicting risk factors of prolonged LOS in patients with aortic dissection (AD). The data of 516 AD patients ...