Name
Abstract | ||
|---|---|---|
Cancer patient data is usually visualized in an aggregated fashion -- e.g., Kaplan-Meier diagrams show the average survival estimate, but leave the viewer uninformed about any special cases. Work with large patient data corpora, e.g. medical web data, often requires both information about the whole corpus as well as detailed information about a single case. The latter is particularly important for the analysis of outliers. We present a method to visualize high dimensional cancer patient data in the form of a two dimensional scatter plot such that both a large scale overview is given and at the same time detailed information about every single patient is displayed. As a projection of high dimensional data onto a space of much lower dimension is bound to reduce information, our method allows to select the most important parameter (survival time) to be preserved in the projection. We present the algorithm and use it to visualize breast cancer patient data. We show the visualizations together with the resulting relevance vectors for an in-depth study. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1882992.1883031 | IHI |
Keywords | Field | DocType |
breast cancer patient data,detailed information,dimensional scatter plot,high dimensional cancer patient,medical web data,time detailed information,cancer patient data,high dimensional data,large patient data corpus,large cancer patient population,single patient,regression analysis,breast cancer,multidimensional scaling | Data mining,Clustering high-dimensional data,Multidimensional scaling,Computer science,Visualization,Outlier,Scatter plot,Cancer | Conference |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sebastian M. Klenk | 1 | 0 | 0.34 |
| Jürgen Dippon | 2 | 5 | 2.82 |
| Peter Fritz | 3 | 7 | 2.10 |
| Gunther Heidemann | 4 | 454 | 48.16 |