@inproceedingsDav2014, Author = {David Korfkamp, Stefan Gudenkauf, Martin Rohde, Eunice Sirri, Joachim Kieschke, H.-Jürgen Appelrath}, Title = {Opening up Data Analysis for Medical Health Services: Cancer Survival Analysis with CARESS}, Year = {2014}, Month = {9}, Editor = {Ladjel Bellatreche, Mukesh K. Mohania}, Publisher = {Springer}, Series = {Lecture Notes in Computer Science}, Edition = {8646}, Booktitle = {Data Warehousing and Knowledge Discovery - 16th International Conference, DaWaK 2014}, Organization = {DEXA}, Doi = {DOI: 10.1007/978-3-319-10160-6_34}, type = {inproceedings}, note = {Dealing with cancer is one of the big challenges of the German healthcare system. Originally, efforts regarding the analysis of cancer data focused on the detection of spatial clusters of cancer incidences. Nowadays, the emphasis also incorporates complex}, Abstract = {Dealing with cancer is one of the big challenges of the German healthcare system. Originally, efforts regarding the analysis of cancer data focused on the detection of spatial clusters of cancer incidences. Nowadays, the emphasis also incorporates complex health services research and quality assurance. In 2013, a law was enacted in Germany forcing the spatially all-encompassing expansion of clinical cancer registries, each of them covering a commuting area of about 1 to 2 million inhabitants. Guidelines for a unified evaluation of data are currently in development, and it is very probable that these guidelines will demand the execution of comparative survival analyses. In this paper, we present how the CARLOS Epidemiological and Statistical Data Exploration System (CARESS), a sophisticated data warehouse system that is used by epidemiological cancer registries (ECRs) in several German federal states, opens up data analysis for a wider audience. We show that by applying the principles of integration and abstraction, CARESS copes with the challenges posed by the diversity of the cancer registry landscape in Germany. Survival estimates are calculated by the software package periodR seamlessly integrated in CARESS. We also discuss several performance optimizations for survival estimation, and illustrate the feasibility of our approach by an experiment on cancer survival estimation performance and by an example on the application of cancer survival analysis with CARESS.} @COMMENTBibtex file generated on