Deep Learning-Based Prediction Modeling of Major Adverse Cardiovascular Events After Liver Transplantation.

ABSTRACT:

Objective

To validate deep learning models' ability to predict post-transplantation major adverse cardiovascular events (MACE) in patients undergoing liver transplantation (LT).

Patients and methods

We used data from Optum's de-identified Clinformatics Data Mart Database to identify liver transplant recipients between January 2007 and March 2020. To predict post-transplantation MACE risk, we considered patients' demographics characteristics, diagnoses, medications, and procedural data recorded back to 3 years before the LT procedure date (index date). MACE is predicted using the bidirectional gated recurrent units (BiGRU) deep learning model in different prediction interval lengths up to 5 years after the index date. In total, 18,304 liver transplant recipients (mean age, 57

SUBMITTER: Abdelhameed A 

PROVIDER: S-EPMC11238640 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature