Using machine learning-based analytics of daily activities to identify modifiable risk factors for falling in Parkinson's disease.

Journal: Parkinsonism & related disorders

PMID: 33249293

Abstract

BACKGROUND: Although risk factors that lead to falling in Parkinson's disease (PD) have been previously studied, the established predictors are mostly non-modifiable. A novel method for fall risk assessment may provide more insight into preventable high-risk activities to reduce future falls.

Authors

Pattamon Panyakaew

Chulalongkorn Centre of Excellence for Parkinson's Disease & Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, 10330, Thailand.
Natapol Pornputtapong

Department of Biochemistry and Microbiology, Faculty of Pharmaceutical Sciences, Chulalongkorn University, 254 Phayathai Road, Pathumwan, Bangkok, 10330, Thailand. natapol.p@chula.ac.th.
Roongroj Bhidayasiri

Keywords

Accidental Falls Activities of Daily Living Age Factors Aged Antiparkinson Agents Female Humans Male Middle Aged Models, Theoretical Parkinson Disease Postural Balance Prognosis Risk Assessment Risk Factors Severity of Illness Index Supervised Machine Learning

External Resources

View on PubMed Access via DOI PubMed (33249293)

Using machine learning-based analytics of daily activities to identify modifiable risk factors for falling in Parkinson's disease.

Abstract

Authors

Keywords

External Resources

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