Explainable Machine Learning for the Assessment of Donor Grafts in Liver Transplantation
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About
Clinically, organ evaluation generally performed by the senior surgeons based on their experience and the visual and tactual inspection of the graft during procurement. However, it is proved that transplant surgeons intuition in the evaluation of donor risk and the estimation of steatosis is inconsistent and usually inaccurate. Besides, graft assessment is a dynamic process refer to amount of complex factors, which is considered to be an incredibly complicated relationship that is nonlinear in nature. Unfortunately, the classical statistic techniques in vogue such as multiple regression require the statistical assumption of independent and linear relationships between explanatory and outcome variables, and fail to analyse a large number of variables. We attempted to develop liver graft assessment models by predicting postoperative DGF using several ML techniques. Secondly, the best prediction model was selected by comparing the performance of different AI algorithms and logistic regression. Finally, we sought to explain the decision made by AI algorithms using a visualization algorithm based on the best prediction model, helping clinicians evaluate specific organ and whether to receive that may develop DGF postoperatively.
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Inclusion criteria
- Age≥18 years-old
- Underwent deceased donor liver transplantation
Exclusion criteria
- Underwent living-donor LT;
- Missing rates of data were more than 80%
Trial design
5,636 participants in 2 patient groups
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Data sourced from clinicaltrials.gov
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