About: Adapting random forests to cope with heavily censored datasets in survival analysis

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #83340 Adapting random forests to cope with heavily censored datasets in survival analysis
http://eprints.org/ontology/hasAccepted	Adapting random forests to cope with heavily censored datasets in survival analysis (PDF)
http://eprints.org/ontology/hasDocument	Adapting random forests to cope with heavily censored datasets in survival analysis (PDF) Adapting random forests to cope with heavily censored datasets in survival analysis (Other) Adapting random forests to cope with heavily censored datasets in survival analysis (Other) Adapting random forests to cope with heavily censored datasets in survival analysis (Other) Adapting random forests to cope with heavily censored datasets in survival analysis (Other) Adapting random forests to cope with heavily censored datasets in survival analysis (Other)
dc:hasVersion	Adapting random forests to cope with heavily censored datasets in survival analysis (PDF)
Title	Adapting random forests to cope with heavily censored datasets in survival analysis
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2020-10
Creator	A. Freitas Tossapol Pomsuwan
status	peer reviewed published
Publisher	dblp
abstract	We address a survival analysis task where the goal is to predict the time passed until a subject is diagnosed with an age-related disease. The main challenge is that subjects’ data are very often censored, i.e., their time to diagnosis is only partly known. We propose a new Random Forest variant to cope with censored data, and evaluate it in experiments predicting the time to diagnosis of 8 age-related diseases, for data from the English Longitudinal Study of Ageing (ELSA) database. In these experiments, the proposed Random Forest variant, in general, outperformed a well-known Random Forest variant for censored data.
Is Part Of	https://kar.kent.ac.uk/id/repository
Subject	Q335 Artificial intelligence
list of authors	https://kar.kent.ac.uk/id/eprint/83340#authors
presented at	2020 European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2020)
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #83340 Adapting random forests to cope with heavily censored datasets in survival analysis

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