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Multiple Regression Analysis in Healthcare Scenario
Introduction
Regression has a number of similarities with the discipline of machine learning when it comes to predicting and forecasting. Multiple linear regression is used to measure the correlation between two or more independent variables and one predictor variable.
Discussion
It is possible to predict hospital length of stay (LOS) using regression analysis. LOS is a helpful metric for managing hospital services and is an index that is assessed for operating expenditures. The collection of clinical and administrative data for enhanced analysis and development work has established itself as a critical support in patient management in all facets of the healthcare industry. In reality, a patients LOS might be impacted by a variety of elements, such as their unique illness, medical demands, or medical history.
An expansion of the single-explanatory variable simple linear regression model is multiple linear regression (MLR). Using a number of independent variables, such as age, gender, or diseases, the MLR model was developed to estimate the value of the dependent variable Y (total LOS). In the example, Y is the total LOS, ²0 is the interception rate, xi is the impartial element, and ²i is the estimations of the independent factors regression coefficients; µ is the model flaw.
MLR is a reliable first step in characterizing the demand and enabling a priori estimation of bed occupancy and utilization of other hospital resources. Additionally, the model performs well, validating its suitability for use as a service-based prediction tool. However, while the linear models interpretation is relatively straightforward, it may not be reliable enough.
Conclusion
The cause-and-effect connection between the variables is considered to remain constant. This presumption might not always be accurate and could produce false results. Future research should therefore focus on enhanced data processing techniques and multicenter trials to validate the model.
Reference
Trunfio, T. A., Scala, A., Giglio, C., Rossi, G., Borrelli, A., Romano, M., & Improta, G. (2022). Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy. BMC Medical Informatics and Decision Making, 22(1), 1-8. Web.
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