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pro vyhledávání: '"Lizotte, Daniel J"'
When modelling data where the response is dichotomous and highly imbalanced, response-based sampling where a subset of the majority class is retained (i.e., undersampling) is often used to create more balanced training datasets prior to modelling. Ho
Externí odkaz:
http://arxiv.org/abs/2410.18144
Autor:
Phelps, Nathan, Marrocco, Stephanie, Cornell, Stephanie, Wolfe, Dalton L., Lizotte, Daniel J.
Reinforcement learning (RL) has helped improve decision-making in several applications. However, applying traditional RL is challenging in some applications, such as rehabilitation of people with a spinal cord injury (SCI). Among other factors, using
Externí odkaz:
http://arxiv.org/abs/2310.14976
Electronic health records (EHRs) include simple features like patient age together with more complex data like care history that are informative but not easily represented as individual features. To better harness such data, we developed an interpret
Externí odkaz:
http://arxiv.org/abs/2204.06076
Autor:
Phelps, Nathan, Marrocco, Stephanie, Cornell, Stephanie, Wolfe, Dalton L., Lizotte, Daniel J.
Publikováno v:
In Intelligence-Based Medicine 2024 9
Akademický článek
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We present an algorithm, Decision-Directed Data Decomposition (D4), which decomposes a dataset into two components. The first contains most of the useful information for a specified supervised learning task. The second orthogonal component contains l
Externí odkaz:
http://arxiv.org/abs/1909.08159
Autor:
Lizotte, Daniel J., Tahmasebi, Arezoo
We develop and evaluate tolerance interval methods for dynamic treatment regimes (DTRs) that can provide more detailed prognostic information to patients who will follow an estimated optimal regime. Although the problem of constructing confidence int
Externí odkaz:
http://arxiv.org/abs/1704.07453
Akademický článek
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Autor:
Rose, Rhiannon V., Lizotte, Daniel J.
When faced with a supervised learning problem, we hope to have rich enough data to build a model that predicts future instances well. However, in practice, problems can exhibit predictive heterogeneity: most instances might be relatively easy to pred
Externí odkaz:
http://arxiv.org/abs/1608.00027
Publikováno v:
In International Journal of Medical Informatics September 2020 141