HYBRID ARTIFICIAL INTELLIGENCE HOSPITAL RESOURCE MANAGEMENT USING CATBOOST

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K.LAVANYA; Dr. R. Yamuna

HYBRID ARTIFICIAL INTELLIGENCE HOSPITAL RESOURCE MANAGEMENT USING CATBOOST

Authors: K.LAVANYA, Dr. R. Yamuna

Unique Paper ID: 175688
Volume: 11
Issue: 11
PageNo: 3523-3526

Keywords: Healthcare decision support systems explainable artificial intelligence machine learning XGBOOST

Abstract:
Efficient bed management is crucial for reducing hospital costs and improving patient outcomes. This study proposes a framework to predict ICU length of stay (LOS) at admission using electronic health records (EHR) and supervised machine learning models. It is the first to employ explainable AI (xAI) for interpreting machine learning predictions using real hospital data. The framework predicts short and long ICU stays, evaluated through metrics such as accuracy, AUC, sensitivity, and F1-score. XGBoost achieved a 98% AUC in predicting LOS. This approach enhances clinical information systems, offering hospitals reliable, explainable LOS predictions to optimize ICU resources and support patient care decisions at admission.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 3523-3526

HYBRID ARTIFICIAL INTELLIGENCE HOSPITAL RESOURCE MANAGEMENT USING CATBOOST

Available:https://ijirt.org/article?manuscript=175688

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Impact Factor
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UGC Approved
Journal no 47859

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