Authors :
Anurag Priyadarshi; Anshumaan Karna
Volume/Issue :
Volume 8 - 2023, Issue 11 - November
Google Scholar :
https://tinyurl.com/4nwntsxf
Scribd :
https://tinyurl.com/39kzbdhf
DOI :
https://doi.org/10.5281/zenodo.10297083
Abstract :
This project aims to enhance hospital
management by predicting patients’ length of stay using
the MIMIC dataset, ultimately resulting in substantial cost
savings and improved resource allocation. In our initial
approach, we categorized the target variable, “length of
stay” into three classes: short, medium, and long.
Employing classification models including Logistic
Regression, Random Forests, and Gradient Boosting, we
attempted to predict patient outcomes. However, the initial
results were unsatisfactory, prompting us to refine our
methodology. We expanded the target variable classes to
five: very short, short, medium, long, and very long,
leading to improved accuracy in predicting short
hospital stays. In the second approach, we treated the
length of stay as a continuous variable and employed
Multiple Linear Regression for modeling. Unfortunately,
this ap- proach yielded sub-optimal results compared to
the classification techniques. We analyzed the encountered
limitations and further propose future steps to enhance
the efficiency and accuracy of prediction models,
ultimately contributing to more effective hospital resource
management.
Keywords :
Length of Stay, MIMIC III, Classification, Random Forest, Healthcare.
This project aims to enhance hospital
management by predicting patients’ length of stay using
the MIMIC dataset, ultimately resulting in substantial cost
savings and improved resource allocation. In our initial
approach, we categorized the target variable, “length of
stay” into three classes: short, medium, and long.
Employing classification models including Logistic
Regression, Random Forests, and Gradient Boosting, we
attempted to predict patient outcomes. However, the initial
results were unsatisfactory, prompting us to refine our
methodology. We expanded the target variable classes to
five: very short, short, medium, long, and very long,
leading to improved accuracy in predicting short
hospital stays. In the second approach, we treated the
length of stay as a continuous variable and employed
Multiple Linear Regression for modeling. Unfortunately,
this ap- proach yielded sub-optimal results compared to
the classification techniques. We analyzed the encountered
limitations and further propose future steps to enhance
the efficiency and accuracy of prediction models,
ultimately contributing to more effective hospital resource
management.
Keywords :
Length of Stay, MIMIC III, Classification, Random Forest, Healthcare.
