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A Unified Data Architecture for Assessing Motor Symptoms in Parkinson's Disease

Standard

A Unified Data Architecture for Assessing Motor Symptoms in Parkinson's Disease. / Gundler, Christopher; Zhu, Qi Rui; Trübe, Leona; Dadkhah, Adrin; Gutowski, Tobias; Rosch, Moritz; Langebrake, Claudia; Nürnberg, Sylvia; Baehr, Michael; Ückert, Frank.

In: Stud Health Technol Inform, Vol. 307, 12.09.2023, p. 22-30.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Gundler, C, Zhu, QR, Trübe, L, Dadkhah, A, Gutowski, T, Rosch, M, Langebrake, C, Nürnberg, S, Baehr, M & Ückert, F 2023, 'A Unified Data Architecture for Assessing Motor Symptoms in Parkinson's Disease', Stud Health Technol Inform, vol. 307, pp. 22-30. https://doi.org/10.3233/SHTI230689

APA

Gundler, C., Zhu, Q. R., Trübe, L., Dadkhah, A., Gutowski, T., Rosch, M., Langebrake, C., Nürnberg, S., Baehr, M., & Ückert, F. (2023). A Unified Data Architecture for Assessing Motor Symptoms in Parkinson's Disease. Stud Health Technol Inform, 307, 22-30. https://doi.org/10.3233/SHTI230689

Vancouver

Gundler C, Zhu QR, Trübe L, Dadkhah A, Gutowski T, Rosch M et al. A Unified Data Architecture for Assessing Motor Symptoms in Parkinson's Disease. Stud Health Technol Inform. 2023 Sep 12;307:22-30. https://doi.org/10.3233/SHTI230689

Bibtex

@article{9a1de8a3893d4b7a8114940a9132d14c,
title = "A Unified Data Architecture for Assessing Motor Symptoms in Parkinson's Disease",
abstract = "INTRODUCTION: The diagnosis and treatment of Parkinson's disease depend on the assessment of motor symptoms. Wearables and machine learning algorithms have emerged to collect large amounts of data and potentially support clinicians in clinical and ambulant settings.STATE OF THE ART: However, a systematical and reusable data architecture for storage, processing, and analysis of inertial sensor data is not available. Consequently, datasets vary significantly between studies and prevent comparability.CONCEPT: To simplify research on the neurodegenerative disorder, we propose an efficient and real-time-optimized architecture compatible with HL7 FHIR backed by a relational database schema.LESSONS LEARNED: We can verify the adequate performance of the system on an experimental benchmark and in a clinical experiment. However, existing standards need to be further optimized to be fully sufficient for data with high temporal resolution.",
keywords = "Humans, Parkinson Disease/diagnosis, Algorithms, Benchmarking, Databases, Factual, Machine Learning",
author = "Christopher Gundler and Zhu, {Qi Rui} and Leona Tr{\"u}be and Adrin Dadkhah and Tobias Gutowski and Moritz Rosch and Claudia Langebrake and Sylvia N{\"u}rnberg and Michael Baehr and Frank {\"U}ckert",
year = "2023",
month = sep,
day = "12",
doi = "10.3233/SHTI230689",
language = "English",
volume = "307",
pages = "22--30",

}

RIS

TY - JOUR

T1 - A Unified Data Architecture for Assessing Motor Symptoms in Parkinson's Disease

AU - Gundler, Christopher

AU - Zhu, Qi Rui

AU - Trübe, Leona

AU - Dadkhah, Adrin

AU - Gutowski, Tobias

AU - Rosch, Moritz

AU - Langebrake, Claudia

AU - Nürnberg, Sylvia

AU - Baehr, Michael

AU - Ückert, Frank

PY - 2023/9/12

Y1 - 2023/9/12

N2 - INTRODUCTION: The diagnosis and treatment of Parkinson's disease depend on the assessment of motor symptoms. Wearables and machine learning algorithms have emerged to collect large amounts of data and potentially support clinicians in clinical and ambulant settings.STATE OF THE ART: However, a systematical and reusable data architecture for storage, processing, and analysis of inertial sensor data is not available. Consequently, datasets vary significantly between studies and prevent comparability.CONCEPT: To simplify research on the neurodegenerative disorder, we propose an efficient and real-time-optimized architecture compatible with HL7 FHIR backed by a relational database schema.LESSONS LEARNED: We can verify the adequate performance of the system on an experimental benchmark and in a clinical experiment. However, existing standards need to be further optimized to be fully sufficient for data with high temporal resolution.

AB - INTRODUCTION: The diagnosis and treatment of Parkinson's disease depend on the assessment of motor symptoms. Wearables and machine learning algorithms have emerged to collect large amounts of data and potentially support clinicians in clinical and ambulant settings.STATE OF THE ART: However, a systematical and reusable data architecture for storage, processing, and analysis of inertial sensor data is not available. Consequently, datasets vary significantly between studies and prevent comparability.CONCEPT: To simplify research on the neurodegenerative disorder, we propose an efficient and real-time-optimized architecture compatible with HL7 FHIR backed by a relational database schema.LESSONS LEARNED: We can verify the adequate performance of the system on an experimental benchmark and in a clinical experiment. However, existing standards need to be further optimized to be fully sufficient for data with high temporal resolution.

KW - Humans

KW - Parkinson Disease/diagnosis

KW - Algorithms

KW - Benchmarking

KW - Databases, Factual

KW - Machine Learning

U2 - 10.3233/SHTI230689

DO - 10.3233/SHTI230689

M3 - SCORING: Journal article

C2 - 37697834

VL - 307

SP - 22

EP - 30

ER -