Deep learning prediction of falls among nursing home residents with Alzheimer's disease.

Journal: Geriatrics & gerontology international

PMID: 32267067

Abstract

AIM: This study aimed to use a convolutional neural network (CNN) to investigate the associations between the time of falling and multiple complicating factors, including age, dementia severity, lower extremity strength and physical function, among nursing home residents with Alzheimer's disease.

Authors

Makoto Suzuki

Department of Pathology, Shizuoka General Hospital, Shizuoka 420-8527, Japan.
Ryosuke Yamamoto

Department of Health Support, Setagaya Municipal Kitazawa En, Setagaya, Japan.
Yuko Ishiguro

Department of Health Support, Setagaya Municipal Kitazawa En, Setagaya, Japan.
Hironori Sasaki

Department of Rehabilitation, Hatsutomi Hoken Hospital, Chiba, Japan.
Harumi Kotaki

Department of Rehabilitation, Hatsutomi Hoken Hospital, Chiba, Japan.

Keywords

Accidental Falls Aged Aged, 80 and over Alzheimer Disease Cohort Studies Deep Learning Female Geriatric Assessment Homes for the Aged Humans Lower Extremity Male Mental Status and Dementia Tests Muscle Strength Nursing Homes Prospective Studies Risk Factors Severity of Illness Index

External Resources

View on PubMed Access via DOI PubMed (32267067)

Deep learning prediction of falls among nursing home residents with Alzheimer's disease.

Abstract

Authors

Keywords

External Resources

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