Recipient survival after organ transplantation

A method for organ transplant prediction model training, comprising:

• receiving a first training dataset relating to a plurality of organ recipients, the first training dataset comprising pre-operative and post-operative factors;
• receiving a second training dataset relating to a plurality of organ donors, the plurality of organ donors corresponding to the plurality of organ recipients;
• filtering the first training dataset to remove post-operative factors, data records for inapplicable recipient treatments, and data records for recipients with graft dysfunction to generate a recipient pre-operative training dataset;
• training a pre-operative organ transplant machine learning model based on the recipient pre-operative training dataset and the second training dataset;
• filtering the first training dataset to remove inapplicable recipient treatments, and recipients with graft dysfunction to generate a recipient post-operative training dataset;
• and
• training a post-operative organ transplant machine learning model based on the recipient post-operative training dataset and the second training dataset, the post-operative machine learning model corresponding to the pre-operative machine learning model.

A system for recipient survival after organ transplant prediction, comprising:

• a memory;
• a processor communicatively coupled to the memory;
• wherein the memory stores a set of instructions which, when executed by the processor, cause the processor to:
• obtain a trained pre-operative organ transplant machine learning model;
• receive a donor dataset corresponding to a plurality of factors relating to a given donor;
• receive a pre-operative recipient dataset corresponding to a plurality of pre-operative recipient factors for a given patient;
• apply the donor dataset and the pre-operative recipient dataset to the trained pre-operative organ transplant machine learning model;
• provide a result for the patient based on the trained organ transplant machine learning model;
• receive a post-operative recipient dataset corresponding to a plurality of post-operative factors, the plurality of post-operative factors relating to a transplantation operation of the patient;
• determine if the patient exhibits graft dysfunction;
• apply the pre-operative recipient dataset and the post-operative recipient dataset to a post-operative organ transplant machine learning model;
• and
• determine a survival probability for the patient.