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1 Σεπ 2024 · The most common forecasting model in health care capacity planning is the ARIMA. •. There is a widespread lack of validation and follow-up on model performance. •. An increased focus on implementation is warranted. Abstract. Background.
13 Ιουλ 2023 · Attempts have frequently been made to raise efficiency and cost-effectiveness in healthcare services. However, to achieve these goals, background knowledge and skill are essential and good understanding of forecasting methods can help healthcare administrators learn, apply, and utilize data to attain their goals.
Propose predictive interpretable analytics models for forecasting healthcare costs using open healthcare data. •. Analyze over 2.3 million patients in New York and build machine learning models to predict costs using diagnosis, severity, and demographics. •.
1 Μαρ 2024 · In this paper, the authors deployed three regression-based ensemble ML models that combine variations of decision trees through Extreme Gradient Boosting (XGBoost), Gradient-boosting Machine (GBM), and Random Forest (RF) methods in predicting medical insurance costs.
It discusses the strengths and weaknesses of various health-forecasting techniques and methods, and then presents the choices available for validating and measuring the accuracy of health-forecasting models.
Forecasting: A Cost Control Tool for Health Care Managers Everette S. Gardner, Jr. and Curtis P. McLaughlin Adapting the appropriate forecasting technique to a health care environment is essential for cost control. As the Chapel Hill experience shows, forecasting is within everyone's reach and can successfully reduce costs. 0361-6274/80/0053 ...
11 Δεκ 2019 · Methods. We used 2014–2015 Swiss health insurance claims data on 373′264 adult patients to classify individuals’ changes in health care costs. We performed extensive feature generation and developed predictive models using logistic regression, boosted decision trees and neural networks.
