Prediction of Neurological Recovery After Cardiac Arrest Using Deep Learning

Cardiac arrest poses a critical challenge in clinical setting, often resulting in uncertain neurological outcomes for survivors. This study explored the use of deep learning methodologies to predict neurological recovery following cardiac arrest. A comprehensive dataset incorporating diverse patient demographics, pre-hospital interventions, and post-resuscitation parameters was employed to develop and validate a deep neural network model. Rigorous model training and validation revealed the model's proficiency in accurately stratifying patients based on their likelihood of neurological recovery. This model showcased robustness in categorizing distinct recovery categories, shedding light on influential pre-hospital and post-resuscitation factors. Ethical considerations were meticulously addressed, emphasizing patient privacy and fairness in model predictions. The proposed methods involve application of Deep learning neural networks to efficiently learn the time series data and accurately classify and distinguish between good and bad outcomes. The study's outcomes present a promising avenue for integrating predictive tools into clinical practice, potentially revolutionizing post-cardiac arrest care by enabling personalized treatment strategies and informed decision-making. Continuous refinement, validation and ethical adherence remain imperative for the responsible translation of predictive models into enhancing patient outcomes in this critical medical domain, with improved ROC-AUC score >0.9 and classification accuracy of ~87%.