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YNHHS Innovation Awards

prediction of death 
Prediction of Death after Terminal Extubation (TE), the Machine Learning Way! 
Lead Ramesh Batra
Team Ramesh Batra, MBBS; Smita Krishnaswamy, PhD
Description Advanced machine learning platform to assist care teams in accurately predicting death after terminal extubation for improving End of Life (EOL) care and boosting organ donation rates
Target Impact Improving the success rate and clinical efficiency of organ donation for transplant centers
Project Lead's Vision Accurate prediction of death after terminal extubation will allow intensivists and administrators to work towards an efficient, ethical and financially robust model whereby the patients will die with dignity within a predictable time-frame and alleviate the suffering for the bereaving families. 

This advanced machine learning model will be groundbreaking in increasing utilization of organs from (Donation after Cardiac Death) DCD organ donors by improving the clinical efficiency of the organ donation process. This will significantly increase the organ donor pool and help bridge the gap between the need for transplant and organ availability.