Artif Intell Med - Predicting patient survival after liver transplantation using evolutionary multi-objective artificial neural networks.


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JECTIVE: The optimal allocation of organs in liver transplantation is a problem that can be resolved using machine-learning techniques. Classical methods of allocation included the assignment of an organ to the first patient on the waiting list without taking into account the characteristics of the donor and/or recipient. In this study, characteristics of the donor, recipient and transplant organ were used to determine graft survival. We utilised a dataset of liver transplants collected by eleven Spanish hospitals that provides data on the survival of patients three months after their operations.METHODS AND MATERIAL: To address the problem of organ allocation, the memetic Pareto evolutionary non-dominated sorting genetic algorithm 2 (MPENSGA2 algorithm), a multi-objective evolutionary algorithm, was used to train radial basis function neural networks, where accuracy was the measure used to evaluate model performance, along with the minimum sensitivity measurement. The neural network models obtained from the Pareto fronts were used to develop a rule-based system. This system will help medical experts allocate organs.RESULTS: The models obtained with the MPENSGA2 algorithm generally yielded competitive results for all performance metrics considered in this work, namely the correct classification rate (C), minimum sensitivity (MS), area under the receiver operating characteristic curve (AUC), root mean squared error (RMSE) and Cohen's kappa (Kappa). In general, the multi-objective evolutionary algorithm demonstrated a better performance than the mono-objective algorithm, especially with regard to the MS extreme of the Pareto front, which yielded the best values of MS (48.98) and AUC (0.5659). The rule-based system efficiently complements the current allocation system (model for end-stage liver disease, MELD) based on the principles of efficiency and equity. This complementary effect occurred in 55% of the cases used in the simulation. The proposed rule-based system minimises the prediction probability error produced by two sets of models (one of them formed by models guided by one of the objectives (entropy) and the other composed of models guided by the other objective (MS)), such that it maximises the probability of success in liver transplants, with success based on graft survival three months post-transplant.CONCLUSION: The proposed rule-based system is objective, because it does not involve medical experts (the expert's decision may be biased by several factors, such as his/her state of mind or familiarity with the patient). This system is a useful tool that aids medical experts in the allocation of organs; however, the final allocation decision must be made by an expert.

Resumo Limpo

jectiv optim alloc organ liver transplant problem can resolv use machinelearn techniqu classic method alloc includ assign organ first patient wait list without take account characterist donor andor recipi studi characterist donor recipi transplant organ use determin graft surviv utilis dataset liver transplant collect eleven spanish hospit provid data surviv patient three month operationsmethod materi address problem organ alloc memet pareto evolutionari nondomin sort genet algorithm mpensga algorithm multiobject evolutionari algorithm use train radial basi function neural network accuraci measur use evalu model perform along minimum sensit measur neural network model obtain pareto front use develop rulebas system system will help medic expert alloc organsresult model obtain mpensga algorithm general yield competit result perform metric consid work name correct classif rate c minimum sensit ms area receiv oper characterist curv auc root mean squar error rmse cohen kappa kappa general multiobject evolutionari algorithm demonstr better perform monoobject algorithm especi regard ms extrem pareto front yield best valu ms auc rulebas system effici complement current alloc system model endstag liver diseas meld base principl effici equiti complementari effect occur case use simul propos rulebas system minimis predict probabl error produc two set model one form model guid one object entropi compos model guid object ms maximis probabl success liver transplant success base graft surviv three month posttransplantconclus propos rulebas system object involv medic expert expert decis may bias sever factor hisher state mind familiar patient system use tool aid medic expert alloc organ howev final alloc decis must made expert

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