detalle del documento
IDENTIFICACIÓN
oai:HAL:hal-02570967v3
Tema
Autoencoders Medical questionnaires Data imputation Parkinson’s Disease PPMI Parkinson's disease [INFO.INFO-NE]Computer Science [cs... [STAT.ML]Statistics [stat]/Machine... [INFO.INFO-AI]Computer Science [cs... [SDV.IB]Life Sciences [q-bio]/Bioe...Autor
Peralta, Maxime Jannin, Pierre Haegelen, Claire Baxter, John, S HLangue
enEditor
HAL CCSD;Elsevier
Categoría
tecnologías: informática
Año
2021
fecha de cotización
15/12/2023
Palabras clave
parkinson disease scienceResumen
International audience; Medical questionnaires are a valuable source of information but are often difficult to analyse due to both their size and the high possibility of having missing values.
This is a problematic issue in biomedical data science as it may complicate how individual questionnaire data is represented for statistical or machine learning analysis.
In this paper, we propose a deeply-learnt residual autoencoder to simultaneously perform non-linear data imputation and dimensionality reduction.
We present an extensive analysis of the dynamics of the performances of this autoencoder regarding the compression rate and the proportion of missing values.
This method is evaluated on motor and non-motor clinical questionnaires of the Parkinson's Progression Markers Initiative (PPMI) database and consistently outperforms linear coupled imputation and reduction approaches.
Peralta, Maxime,Jannin, Pierre,Haegelen, Claire,Baxter, John, S H, 2021, Data Imputation and Compression For Parkinson's Disease Clinical Questionnaires, HAL CCSD;Elsevier
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