Random Survival Forest for Censored Functional Data
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915165777166336 |
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| author | Romano, Elvira Loffredo, Giuseppe Maturo, Fabrizio |
| author_facet | Romano, Elvira Loffredo, Giuseppe Maturo, Fabrizio |
| contents | This paper introduces a Random Survival Forest (RSF) method for functional data. The focus is specifically on defining a new functional data structure, the Censored Functional Data (CFD), for dealing with temporal observations that are censored due to study limitations or incomplete data collection. This approach allows for precise modelling of functional survival trajectories, leading to improved interpretation and prediction of survival dynamics across different groups. A medical survival study on the benchmark SOFA data set is presented. Results show good performance of the proposed approach, particularly in ranking the importance of predicting variables, as captured through dynamic changes in SOFA scores and patient mortality rates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_15340 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Random Survival Forest for Censored Functional Data Romano, Elvira Loffredo, Giuseppe Maturo, Fabrizio Methodology Machine Learning 62R10, 62N02, 62P10 G.3 This paper introduces a Random Survival Forest (RSF) method for functional data. The focus is specifically on defining a new functional data structure, the Censored Functional Data (CFD), for dealing with temporal observations that are censored due to study limitations or incomplete data collection. This approach allows for precise modelling of functional survival trajectories, leading to improved interpretation and prediction of survival dynamics across different groups. A medical survival study on the benchmark SOFA data set is presented. Results show good performance of the proposed approach, particularly in ranking the importance of predicting variables, as captured through dynamic changes in SOFA scores and patient mortality rates. |
| title | Random Survival Forest for Censored Functional Data |
| topic | Methodology Machine Learning 62R10, 62N02, 62P10 G.3 |
| url | https://arxiv.org/abs/2407.15340 |