Development of a structural equation modeling‐based decision tree methodology for the analysis of lung transplantations

Lung transplantation has a vital role among all organ transplant procedures since it is the only accepted treatment for the end‐stage pulmonary failure. There have been several research attempts to model the performance of lung transplants. Yet, these early studies either lack model predictive capability by relying on strong statistical assumptions or provide adequate predictive capability but suffer from less interpretability to the medical professionals. The proposed method described in this paper is focused on overcoming these limitations by providing a structural equation modeling‐based decision tree construction procedure for lung transplant performance evaluation. Specifically, partial least squares‐based path modeling algorithm is used for the structural equation modeling part. The proposed method is validated through a US nation‐wide dataset obtained from United Network for Organ Sharing (UNOS). The results are promising in terms of both prediction and interpretation capabilities, and are superior to the existing techniques. Hence, we assert that a decision support system, which is based on the proposed method, can bridge the knowledge‐gap between the large amount of available data and betterment of the lung transplantation procedures.