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Titolo:
Machine learning for survival analysis: a case study on recurrence of prostate cancer
Autore:
Zupan, B; Demsar, J; Kattan, MW; Beck, JR; Bratko, I;
Indirizzi:
Univ Ljubljana, Fac Comp & Informat Sci, SI-1000 Ljubljana, Slovenia Univ Ljubljana Ljubljana Slovenia SI-1000 i, SI-1000 Ljubljana, Slovenia Jozef Stefan Inst, Ljubljana, Slovenia Jozef Stefan Inst Ljubljana Slovenia f Stefan Inst, Ljubljana, Slovenia Baylor Coll Med, Houston, TX 77030 USA Baylor Coll Med Houston TX USA 77030 ylor Coll Med, Houston, TX 77030 USA Mem Sloan Kettering Canc Ctr, New York, NY 10021 USA Mem Sloan Kettering Canc Ctr New York NY USA 10021 New York, NY 10021 USA
Titolo Testata:
ARTIFICIAL INTELLIGENCE IN MEDICINE
fascicolo: 1, volume: 20, anno: 2000,
pagine: 59 - 75
SICI:
0933-3657(200008)20:1<59:MLFSAA>2.0.ZU;2-O
Fonte:
ISI
Lingua:
ENG
Soggetto:
RADICAL PROSTATECTOMY; DISEASE RECURRENCE; REGRESSION; NOMOGRAM;
Keywords:
survival analysis; censored data; machine learning; data weighting; prostate cancer recurrence; outcome prediction after radical prostatectomy; prognostic models in medicine;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Clinical Medicine
Citazioni:
25
Recensione:
Indirizzi per estratti:
Indirizzo: Zupan, B Univ Ljubljana, Fac Comp & Informat Sci, Trazaska 25, SI-1000 Ljubljana, Slovenia Univ Ljubljana Trazaska 25 Ljubljana Slovenia SI-1000 , Slovenia
Citazione:
B. Zupan et al., "Machine learning for survival analysis: a case study on recurrence of prostate cancer", ARTIF INT M, 20(1), 2000, pp. 59-75

Abstract

Machine learning techniques have recently received considerable attention,especially when used for the construction of prediction models from data. Despite their potential advantages over standard statistical methods, like their ability to model non-linear relationships and construct symbolic and interpretable models, their applications to survival analysis are at best rare, primarily because of the difficulty to appropriately handle censored data. In this paper we propose a schema that enables the use of classification methods - including machine learning classifiers - for survival analysis. To appropriately consider the follow-up time and censoring, we propose a technique that, for the patients for which the event did not occur and haveshort follow-up times, estimates their probability of event and assigns them a distribution of outcome accordingly. Since most machine learning techniques do not deal with outcome distributions, the schema is implemented using weighted examples. To show the utility of the proposed technique, we investigate a particular problem of building prognostic models for prostate cancer recurrence, where the sole prediction of the probability of event (andnot its probability dependency on time) is of interest. A case study on preoperative and postoperative prostate cancer recurrence prediction shows that by incorporating this weighting technique the machine learning tools stand beside modern statistical methods and may, by inducing symbolic recurrence models, provide further insight to relationships within the modeled data. (C) 2000 Elsevier Science B.V. All rights reserved.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 27/11/20 alle ore 22:08:29