Chronic kidney disease affects from 3% to 17% of the population across European Union. 1,2 Regarding health expenditure, for example, Germany is facing more than €3 billion of costs for patients with chronic kidney failure. Therefore, it requires a very costly therapy for the individuals.
Up to 17 %
> €3 billion
public cost in Germany
Costly therapy for people
Led by Charité, this pilot uses the results of the ongoing MACSS project to improve outcomes and reduce costs after kidney transplantation. Thus intervention, driven by novel dynamic prediction models and alert systems will allow early recognition, management, and prevention of post-transplant complications.
In order to deal with the variety of large data sources, reliable natural language processing (NLP) and machine learning (ML) methods are required to allow early prediction of complications and thus facilitate early intervention to prevent morbidity and hospitalizations.
1 Source: The global issue of kidney disease. Lancet. 2013 Jul 13;382(9887):101. doi: 10.1016/S0140-6736(13)61545-7.
2 Source: CKD Prevalence Varies across the European General Population. Brück K et al. J Am Soc Nephrol. 2016 Jul;27(7):2135-47.