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PSICA :
PSICA : Decision trees for probabilistic subgroup identification with categorical treatments
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- Sysoev, Oleg, 1981- (författare)
- Linköpings universitet,Statistik och maskininlärning,Filosofiska fakulteten,Machine Learning
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- Bartoszek, Krzysztof (författare)
- Linköpings universitet,Statistik och maskininlärning,Filosofiska fakulteten
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- Ekström, Eva-Charlotte, 1956- (författare)
- Uppsala universitet,Internationell mödra- och barnhälsovård (IMCH),Uppsala University, Akademiska Sjukhuset, Uppsala, Sweden
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- Ekholm Selling, Katarina, 1976- (författare)
- Uppsala universitet,Internationell mödra- och barnhälsovård (IMCH)
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- Ekström Selling, Katarina (författare)
- Uppsala University, Akademiska Sjukhuset, Uppsala, Sweden
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(creator_code:org_t)
- 2019-06-27
- 2019
- Engelska.
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Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 38:22, s. 4436-4452
- Relaterad länk:
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Personalized medicine aims at identifying best treatments for a patient with given characteristics. It has been shown in the literature that these methods can lead to great improvements in medicine compared to traditional methods prescribing the same treatment to all patients. Subgroup identification is a branch of personalized medicine, which aims at finding subgroups of the patients with similar characteristics for which some of the investigated treatments have a better effect than the other treatments. A number of approaches based on decision trees have been proposed to identify such subgroups, but most of them focus on two-arm trials (control/treatment) while a few methods consider quantitative treatments (defined by the dose). However, no subgroup identification method exists that can predict the best treatments in a scenario with a categorical set of treatments. We propose a novel method for subgroup identification in categorical treatment scenarios. This method outputs a decision tree showing the probabilities of a given treatment being the best for a given group of patients as well as labels showing the possible best treatments. The method is implemented in an R package psica available on CRAN. In addition to a simulation study, we present an analysis of a community-based nutrition intervention trial that justifies the validity of our method.
Ämnesord
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- bootstrap
- decision trees
- personalized medicine
- random forest
- subgroup discovery
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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