A Unified Data Architecture for Assessing Motor Symptoms in Parkinson's Disease
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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, Jahrgang 307, 12.09.2023, S. 22-30.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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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 -