Neural Network Modeling of Long-Term Cardiac Arrest Risk Forecasting

Abstract

It has a look at examines revolutionary neural network modeling of lengthy-time period cardiac arrest hazard forecasting. We generated a comprehensive dataset of cardiac arrest sufferers and used a bidirectional lengthy brief-term memory (Bi-LSTM) version to evaluate the risk. Our effects tested that the Bi-LSTM version outperformed conventional machine-studying techniques such as logistic regression and boosted trees in phrases of accuracy and sensitivity. We also used a visualization approach to interpret version predictions, which indicated that our model became capable of appropriately picking out affected person traits associated with cardiac arrest hazards. We concluded that our model could provide practical long-time period chance estimation for cardiac arrest sufferers and may be used for manual scientific interventions and prevent cardiac arrests in clinical contexts.