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 %
population affected
> €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.
Schmidt, D., Osmanodja, B., Pfefferkorn, M., Graf, V., Raschke, D., Duettmann, W., Naik, M. G., Gethmann, C. J., Mayrdorfer, M., Halleck, F., Liefeldt, L., Glander, P., Staeck, O., Mallach, M., Peuker, M., Budde, K. TBase – an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients. J. Vis. Exp. (170), e61971, doi:10.3791/61971 (2021).
Duettmann, W., Naik, M. G., Schmidt, D., Pfefferkorn, M., Kurz, M., Graf, V., Kreichgauer, A., Hoegl, S., Haenska, M., Gielsdorf, T., Breitenstein, T., Osmanodja, B., Glander, P., Bakker, J., Mayrdorfer, M., Gethmann, C. J., Bachmann, F., Choi, M., Schrezenmeier, E., Zukunft, B., Halleck, F., Budde, K. Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform. J. Vis. Exp. (170), e61899, doi:10.3791/61899 (2021).
Duettmann, W., Naik, M.G., Zukunft, B., Osmonodja, B., Bachmann, F., Choi, M., Roller, R., Mayrdorfer, M., Halleck, F., Schmidt, D. and Budde, K. (2021), eHealth in transplantation. Transpl. Int., 34: 16-26. https://doi.org/10.1111/tri.13778
Poster at BigMedilytics event: “Big Data: Fueling the transformation of Europe’s Healthcare Sector”. September 4-5, 2019, Valencia, Spain
Vashisth G, Voigt-Antons J-N, Mikhailov M, Roller R. Exploring Diachronic Changes of Biomedical Knowledge using Distributed Concept Representations. Proceedings of the 18th BioNLP Workshop and Shared Task, 2019, Page(s) 348-358. DOI: 10.18653/v1/w19-5037
Freitas da Cruz, H., Horschig, S., Nusshag, C., Schapranow, M.-P. (2019). Knowledge Distillation from Machine Learning Models for Prediction of Hemodialysis Outcomes. International Journal On Advances in Life Sciences, 11 (12), 33-43.
da Cruz H.F., Pfahringer B., Schneider F., Meyer A., Schapranow MP. (2019) External Validation of a “Black-Box” Clinical Predictive Model in Nephrology: Can Interpretability Methods Help Illuminate Performance Differences? In: Riaño D., Wilk S., ten Teije A. (eds) Artificial Intelligence in Medicine. AIME 2019. Lecture Notes in Computer Science, vol 11526. Springer, Cham
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.