Maristella Lunardon, Daniela Lucangeli, Marco Zorzi, Francesco Sella
Math computerized games in the classroom: A number line training in primary school children
Progress in brain research
organ transplantation accurate analysis clinical outcomes requires large highquality data sets outcomes influenced multitude factors donor recipient transplant characteristics posttransplant events may also change time although large data sets already exist continually expanding transplant registries health institutions data rarely combined analysis lack harmonization promoted digitalization healthcare sector effective data harmonization tools became available potential applications also organ transplantation discuss herein present problems harmonization organ transplant data offer solutions enhance accuracy use emerging new tools overcome problem inadequate representation transplantationspecific terms ontologies common data models particular field could created supported consortium related stakeholders ensure broad acceptance adopting clear datasharing policies diminish administrative barriers impede collaboration organizations secure multiparty computation frameworks artificial intelligence ai approach federated learning facilitate decentralized harmonized analysis data sets without sharing sensitive data compromising patient privacy common image data model built upon standardized format would beneficial aibased analysis pathology images implementation promising new tools measures ideally involvement support transplant societies expected produce improved integration harmonization transplant data greater accuracy clinical decisionmaking enabling improved patient outcomes