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Health Care Costs

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Predicting high-need high-cost pediatric hospitalized patients in China based on machine learning methods.

Scientific reports
Rapidly increasing healthcare spending globally is significantly driven by high-need, high-cost (HNHC) patients, who account for the top 5% of annual healthcare costs but over half of total expenditures. The programs targeting existing HNHC patients ...

Enhancing Healthcare Through Telehealth Ecosystems: Impacts and Prospects.

Studies in health technology and informatics
This poster presents a comprehensive assessment of the transformative potential of telehealth ecosystems, integrating Internet of Things (IoT), Internet of Medical Things (IoMT), and Artificial Intelligence (AI) technologies. The study explores their...

Machine learning models for assessing risk factors affecting health care costs: 12-month exercise-based cardiac rehabilitation.

Frontiers in public health
INTRODUCTION: Exercise-based cardiac rehabilitation (ECR) has proven to be effective and cost-effective dominant treatment option in health care. However, the contribution of well-known risk factors for prognosis of coronary artery disease (CAD) to p...

Risk adjustment for regional healthcare funding allocations with ensemble methods: an empirical study and interpretation.

The European journal of health economics : HEPAC : health economics in prevention and care
We experiment with recent ensemble machine learning methods in estimating healthcare costs, utilizing Finnish data containing rich individual-level information on healthcare costs, socioeconomic status and diagnostic data from multiple registries. Ou...

Enhancing Stress Detection: A Comprehensive Approach through rPPG Analysis and Deep Learning Techniques.

Sensors (Basel, Switzerland)
Stress has emerged as a major concern in modern society, significantly impacting human health and well-being. Statistical evidence underscores the extensive social influence of stress, especially in terms of work-related stress and associated healthc...

A systematic review of economic evaluation of artificial intelligence-based screening for eye diseases: From possibility to reality.

Survey of ophthalmology
Artificial Intelligence (AI) has become a focus of research in the rapidly evolving field of ophthalmology. Nevertheless, there is a lack of systematic studies on the health economics of AI in this field. We examine studies from the PubMed, Google Sc...

Predictors of High Healthcare Cost Among Patients with Generalized Myasthenia Gravis: A Combined Machine Learning and Regression Approach from a US Payer Perspective.

Applied health economics and health policy
BACKGROUND: High healthcare costs could arise from unmet needs. This study used random forest (RF) and regression methods to identify predictors of high costs from a US payer perspective in patients newly diagnosed with generalized myasthenia gravis ...

The Impact of Artificial Intelligence in Reducing the Cost of Dementia: A Scoping Review.

Studies in health technology and informatics
INTRODUCTION: Dementia is a major cause of disability among the elderly, imposing significant financial burdens on healthcare systems. Traditional care approaches contribute to rising costs, especially in high-income countries. Artificial intelligenc...

Determining health care cost drivers in older Hodgkin lymphoma survivors using interpretable machine learning methods.

Journal of managed care & specialty pharmacy
BACKGROUND: The cost of health care for patients with Hodgkin lymphoma (HL) is projected to rise, making it essential to understand expenditure drivers across different demographics, including the older adult population. Although older HL patients co...

Comparison of methods for tuning machine learning model hyper-parameters: with application to predicting high-need high-cost health care users.

BMC medical research methodology
BACKGROUND: Supervised machine learning is increasingly being used to estimate clinical predictive models. Several supervised machine learning models involve hyper-parameters, whose values must be judiciously specified to ensure adequate predictive p...