首页|Universite Sorbonne Paris Nord Reports Findings in Experimental Lung Transplants (Novel dimensionality reduction method, Taelcore, enhances lung transplantation risk prediction)
Universite Sorbonne Paris Nord Reports Findings in Experimental Lung Transplants (Novel dimensionality reduction method, Taelcore, enhances lung transplantation risk prediction)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Transplant Medicine - Experimental Lung Transplants is the subject ofa report. According to news reporting originating in Villetaneuse, France, by NewsRx journalists, researchstated, “In this work, we present a new approach to predict the risk of acute cellular rejection (ACR)after lung transplantation by using machine learning algorithms, such as Multilayer Perceptron (MLP)or Autoencoder (AE), and combining them with topological data analysis (TDA) tools. Our proposedmethod, named topological autoencoder with best linear combination for optimal reduction of embeddings(Taelcore), effectively reduces the dimensionality of high-dimensional datasets and yields better resultscompared to other models.”
VilletaneuseFranceEuropeBiomedicineCyborgsDimensionalityReductionEmerging TechnologiesExperimental Lung TransplantsHealth and MedicineMachine LearningRisk and PreventionSurgeryTransplant Medicine
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