Symptom-based classification of Parkinson’s disease using retrospective electronic medical records

This cross-sectional study of electronic medical records (EMRs) was conducted to investigate symptom patterns associated with Parkinson's disease (PD) duration and severity and to explore a symptom-based classification of PD. Data from 334 patients with idiopathic PD who visited a single hospital in South Korea between 2011 and 2021, were used to extract a range of PD-associated symptoms and traditional East-Asian medicine (TEAM) symptoms. Frequency analysis was performed to identify key clinical symptoms by PD progression and severity. Patients with moderate disease severity exhibited different symptom patterns across disease-duration categories, including differences in dyskinesia, speech disorder, floating pulse, and yellow tongue coating. Hierarchical clustering based on PD-related and traditional East Asian medicine symptoms identified 5 distinct EMR-based subtypes. EMR subtype 1 was characterized by the mildest disease severity and shortest disease duration, whereas the remaining subtypes showed distinct symptom profiles and a generally greater overall symptom burden. This information may be useful for identifying clinically distinguishable EMR-based PD subtypes and symptom patterns associated with disease duration and severity. These preliminary findings provide a basis for future studies investigating whether such patterns may inform individualized approaches to PD management.

Authors

Institutions

Publication Details

Journal
Medicine
Published
2026-09-18
DOI
https://doi.org/10.1097/md.0000000000050608
Primary Topic
Parkinson's Disease Mechanisms and Treatments
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Symptom-based classification of Parkinson’s disease using retrospective electronic medical records

Eunbyul Cho, 차지윤, Seong-Uk Park, Jae Young Jang et al.
Medicine
Parkinson's Disease Mechanisms and Treatments
article

Symptom-based classification of Parkinson’s disease using retrospective electronic medical records

Eunbyul Cho, 차지윤, Seong-Uk Park, Jae Young Jang, Ye-chae Hwang, Jung-Hee Jang, Eun Kyoung Ahn, HuiYan Zhao, Gyu Ri Jeon, Ojin Kwon
article en

Abstract

This cross-sectional study of electronic medical records (EMRs) was conducted to investigate symptom patterns associated with Parkinson's disease (PD) duration and severity and to explore a symptom-based classification of PD. Data from 334 patients with idiopathic PD who visited a single hospital in South Korea between 2011 and 2021, were used to extract a range of PD-associated symptoms and traditional East-Asian medicine (TEAM) symptoms. Frequency analysis was performed to identify key clinical symptoms by PD progression and severity. Patients with moderate disease severity exhibited different symptom patterns across disease-duration categories, including differences in dyskinesia, speech disorder, floating pulse, and yellow tongue coating. Hierarchical clustering based on PD-related and traditional East Asian medicine symptoms identified 5 distinct EMR-based subtypes. EMR subtype 1 was characterized by the mildest disease severity and shortest disease duration, whereas the remaining subtypes showed distinct symptom profiles and a generally greater overall symptom burden. This information may be useful for identifying clinically distinguishable EMR-based PD subtypes and symptom patterns associated with disease duration and severity. These preliminary findings provide a basis for future studies investigating whether such patterns may inform individualized approaches to PD management.

MedicineVol. 105(38)
Daejeon University (KR), Korea University of Technology and Education (KR), Kyung Hee University (KR), Korea Institute of Oriental Medicine (KR), Wonkwang University (KR)
Openalex Percentile: Top 11%
Parkinson's Disease Mechanisms and Treatments
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.