Computational Intelligence Toward Risk Analysis in Liver Transplantation

Abstract

Recent years have seen a phenomenal change in healthcare paradigms and data analytics along with computational intelligence has been a key player in this field. One of the main objectives of incorporating computational intelligence in healthcare analytics is to obtain better insights about the patients and proffer more efficient treatment. This work is focused to the National Liver Transplant Program of Uruguay, where comprehensive health indicators of the patients were considered and analyzed for intelligent risk prediction and profiling. The principal objective has been the separation of the cohort in risk-groups using computational intelligence, thereby facilitating efficient analysis of the patients assessed under the liver transplantation program in a predictive pre-transplant perspective. In this respect, clustering algorithms were applied on the cohort considering their comprehensive health-data at the entry-point evaluation and the clusters obtained were analyzed. The clusters showed distinctive properties pertaining to risks, providing predictive models and detailed risk-profiling of the patients in the pre-transplant stage. Also, this marks the foundation of Clinical Decision Support Systems in liver transplantation, which act as an assistive tool for the medical personnel in getting deeper insights to patient health in advance and leads to the holistic visualization of the healthcare scenario, helping to choosing a more efficient and personalized treatment strategy.