PortalPublikationenContinuous, Learned Imputation of Missing Values in Parkinso...

Continuous, Learned Imputation of Missing Values in Parkinson's Disease

Standard

Continuous, Learned Imputation of Missing Values in Parkinson's Disease. / Gundler, Christopher; Pötter-Nerger, Monika.

in: Stud Health Technol Inform, Jahrgang 316, 22.08.2024, S. 654-658.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Gundler, C & Pötter-Nerger, M 2024, 'Continuous, Learned Imputation of Missing Values in Parkinson's Disease', Stud Health Technol Inform, Jg. 316, S. 654-658. https://doi.org/10.3233/SHTI240499

APA

Gundler, C., & Pötter-Nerger, M. (2024). Continuous, Learned Imputation of Missing Values in Parkinson's Disease. Stud Health Technol Inform, 316, 654-658. https://doi.org/10.3233/SHTI240499

Vancouver

Gundler C, Pötter-Nerger M. Continuous, Learned Imputation of Missing Values in Parkinson's Disease. Stud Health Technol Inform. 2024 Aug 22;316:654-658. https://doi.org/10.3233/SHTI240499

Bibtex

@article{3eb2f1a657dc4ab3921fffd3fb40dfa8,
title = "Continuous, Learned Imputation of Missing Values in Parkinson's Disease",
abstract = "Parkinson's disease management requires accurate clinical scores but suffers from missing data. Leveraging self-supervised learning, we demonstrate superior generalization capabilities across populations compared to other well-established imputation techniques (MIWAE, MissForest, MICE). With the ability to employ the method already during the data collection and not afterward, the technology allows more robust data collection in clinical reality.",
keywords = "Parkinson Disease, Humans, Supervised Machine Learning",
author = "Christopher Gundler and Monika P{\"o}tter-Nerger",
year = "2024",
month = aug,
day = "22",
doi = "10.3233/SHTI240499",
language = "English",
volume = "316",
pages = "654--658",

}

RIS

TY - JOUR

T1 - Continuous, Learned Imputation of Missing Values in Parkinson's Disease

AU - Gundler, Christopher

AU - Pötter-Nerger, Monika

PY - 2024/8/22

Y1 - 2024/8/22

N2 - Parkinson's disease management requires accurate clinical scores but suffers from missing data. Leveraging self-supervised learning, we demonstrate superior generalization capabilities across populations compared to other well-established imputation techniques (MIWAE, MissForest, MICE). With the ability to employ the method already during the data collection and not afterward, the technology allows more robust data collection in clinical reality.

AB - Parkinson's disease management requires accurate clinical scores but suffers from missing data. Leveraging self-supervised learning, we demonstrate superior generalization capabilities across populations compared to other well-established imputation techniques (MIWAE, MissForest, MICE). With the ability to employ the method already during the data collection and not afterward, the technology allows more robust data collection in clinical reality.

KW - Parkinson Disease

KW - Humans

KW - Supervised Machine Learning

U2 - 10.3233/SHTI240499

DO - 10.3233/SHTI240499

M3 - SCORING: Journal article

C2 - 39176827

VL - 316

SP - 654

EP - 658

ER -