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\pard\sa200\sl276\slmult1\f0\fs22\lang22 Our Random Forest model harnesses the power of the Acute Physiology and Chronic Health Evaluation (APACHE) scoring system to predict ICU length of stay with precision. This advanced approach seamlessly integrates various admission inputs, optimizing predictive accuracy. Key variables include demographic factors such as age, ethnicity, and gender, alongside clinical indicators like elective surgery and pre-ICU length of stay. Furthermore, physiological markers like weight, albumin, bilirubin, and blood urea nitrogen, all derived from APACHE, contribute to the model's robustness.\par
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Incorporating intricate details such as the Glasgow Coma Scale assessments, glucose levels, and vital signs like heart rate and respiratory rate, our model captures a comprehensive snapshot of the patient's condition upon admission. Additionally, it accounts for critical factors such as intubation status, ventilator use, and potential complications like AIDS, cirrhosis, and diabetes mellitus.\par
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With an expansive dataset encompassing diverse patient profiles, including those with leukemia, lymphoma, or solid tumors with metastasis, our model ensures broad applicability across various medical scenarios. Ultimately, by leveraging the APACHE III-J Bodysystem classification, our Random Forest model stands at the forefront of predictive analytics, empowering healthcare providers with invaluable insights for informed decision-making in critical care settings.\par
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