Liver transplantation is a lifesaving procedure for those suffering from end stage cirrhosis, liver failure, or a cancerous lesion of the liver. Unfortunately, there is a shortage of available liver grafts. Only in the US, every year nearly 14,000 patients are listed and waiting for orthotopic liver transplantation (OLT) while 3,000 patients are dying or increasing illness before getting a liver transplant.

Professor Anna Farzindar, research associate and Health Informatics advisor of IMSC, brings this challenge to her Data Informatics Professional Practicum (INF 560) courseat USC. Student teams up to work on medical data analytic challenges in collaboration with Keck School of Medicine. In the Spring Semester 2018, five teams analyzed 88,000 anonymized patients’ data to provide the innovative solutions for Optimizing the Success of Organ Allocation and Prediction of Graft Futility in Liver Transplantation Patients. Machine learning can provide a complementary solution to current numerical scale of Model for End-Stage Liver Disease (MELD) to prioritize patients who are waiting for a liver transplant.