Détail du document
Identifiant
oai:HAL:hal-02570967v3
Sujet
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...Auteur
Peralta, Maxime Jannin, Pierre Haegelen, Claire Baxter, John, S HLangue
enEditeur
HAL CCSD;Elsevier
Catégorie
technologies : sciences informatique
Année
2021
Date de référencement
15/12/2023
Mots clés
parkinson disease scienceRésumé
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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