Disease prediction using existing deep learning methods can predict diseases with relatively limited clinical data by detecting the symptoms of specific parts and body abnormalities according to the disease. Predicting a patient’s severity requires considering multiple types of clinical data. However, existing models struggle with overfitting and reduced accuracy in making these predictions. This study proposes a system that can predict the signs of critical illness using vital data from multiple patients, such as respiratory rate and oxygen saturation, through deep learning. This enables an early response by medical staff, improving treatment efficiency, and reducing the mortality rate. To address these issues, DNN-based intensive care prediction (DBICP), was used to detect whether the patient is improving and the risk; it showed a prediction accuracy of 95%, which is approximately 10% higher than the prediction accuracy using existing methods. If the predicted patient status is presented to the medical staff through the proposed system, we expect that work efficiency and treatment results will be improved through a more accurate and faster diagnosis.

Keywords: Deep learning, Healthcare, Critical patient condition

No potential conflict of interest relevant to this article was reported.