Optimizing Length of Stay Prediction After Intubation: An Advanced Machine Learning Model with Real-time Vital Sign Integration

In the critical care unit, bedside monitors track patients’ vital signs to help physicians make choices. With increased storage and analysis capability, huge datasets may be processed faster. Lately, physiological data have showed promise for identifying specific consequences and occurrences. At the critical care unit. Our method was aimed to Live bedside vital sign data may be automatically retrieved. Devices that can predict patient mechanical ventilation for more than 4 days in a pediatric critical care unit. 284 ICU patients had their vital signs and clinical history documented continuously from admission. 24 hours of artificial breathing in Boston Children’s Hospital’s pediatric medical-surgical critical care unit. They trained many machine learning models using subsets. Assess the likelihood of each person staying long. We examined our systems’ capacity to anticipate outcomes for unknown persons. Adding vital sign data increased model performance to 90% (area under the curve) from >83% when using just vital sign data. static participant clinical data in electronic health records. For research group representative sample reliability, we employed this approach on 300 individually trained tests employing a variety of sets for training and hold-out validation. Other deep learning methods failed to forecast like ours. Gaining equivalent work outcomes prediction skills. We may be able to help create scalable ICU-based prediction systems that leverage real-time patient monitor data.