Machine learning for survival analysis: A case study on recurrence of prostate cancer Conference Paper
| Authors: | Zupan, B.; Demšar, J.; Kattan, M. W.; Beck, J. R.; Bratko, I. |
| Editors: | Horn, W.; Shahar, Y.; Lindberg, G.; Andreassen, S.; Wyatt, J. |
| Title: | Machine learning for survival analysis: A case study on recurrence of prostate cancer |
| Conference Title: | 7th Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making (AIMDM’99) |
| Abstract: | This paper deals with the problem of learning prognostic models from medical survival data, where the sole prediction of probability of event (and not its probability dependency on time) is of interest. To appropriately consider the follow-up time and censoring - both characteristic for survival data - we propose a weighting technique that lessens the impact of data from patients for which the event did not occur and have short follow-up times. A case study on prostate cancer recurrence shows that by incorporating this weighting technique the machine learning tools stand beside or even outperform modern statistical methods and may, by inducing symbolic recurrence models, provide further insight to relationships within the modeled data. © Springer-Verlag Berlin Heidelberg 1999. |
| Keywords: | survival analysis; artificial intelligence; urology; decision making; medicine; prostate cancers; diseases; prostate cancer recurrence; survival data; prognostic model; learning systems; a-weighting; probability dependencies; weighting techniques |
| Journal Title | Lecture Notes in Computer Science |
| Volume: | 1620 |
| Conference Dates: | 1999 June 20-24 |
| Conference Location: | Aalborg, Denmark |
| ISBN: | 0302-9743 |
| Publisher: | Springer |
| Date Published: | 1999-06-11 |
| Start Page: | 346 |
| End Page: | 355 |
| Language: | English |
| PROVIDER: | scopus |
| DOI: | 10.1007/3-540-48720-4_37 |
| DOI/URL: | |
| Notes: | Article in "Artificial Intelligence in Medicine" (ISBN: 978-3-540-66162-7) -- Conference Paper -- Conference code: 148009 -- Export Date: 16 August 2016 -- Source: Scopus |
