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Mishra AK, Skubic M, Despins LA, Popescu M, Keller J, Rantz M, Abbott C, Enayati M, Shalini S, Miller S. Explainable Fall Risk Prediction in Older Adults Using Gait and Geriatric Assessments. Front Digit Health 2022; 4:869812. [PMID: 35601885 PMCID: PMC9120414 DOI: 10.3389/fdgth.2022.869812] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
Older adults aged 65 and above are at higher risk of falls. Predicting fall risk early can provide caregivers time to provide interventions, which could reduce the risk, potentially avoiding a possible fall. In this paper, we present an analysis of 6-month fall risk prediction in older adults using geriatric assessments, GAITRite measurements, and fall history. The geriatric assessments included were Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), Mini-Mental State Examination (MMSE), Geriatric Depression Scale (GDS), and Short Form 12 (SF12). These geriatric assessments are collected by staff nurses regularly in senior care facilities. From the GAITRite assessments on the residents, we included the Functional Ambulatory Profile (FAP) scores and gait speed to predict fall risk. We used the SHAP (SHapley Additive exPlanations) approach to explain our model predictions to understand which predictor variables contributed to increase or decrease the fall risk for an individual prediction. In case of a high fall risk prediction, predictor variables that contributed the most to elevate the risk could be further examined by the health providers for more personalized health interventions. We used the geriatric assessments, GAITRite measurements, and fall history data collected from 92 older adult residents (age = 86.2 ± 6.4, female = 57) to train machine learning models to predict 6-month fall risk. Our models predicted a 6-month fall with an AUC of 0.80 (95% CI of 0.76–0.85), sensitivity of 0.82 (95% CI of 0.74–0.89), specificity of 0.72 (95% CI of 0.67–0.76), F1 score of 0.76 (95% CI of 0.72–0.79), and accuracy of 0.75 (95% CI of 0.72–0.79). These results show that our early fall risk prediction method performs well in identifying residents who are at higher fall risk, which offers care providers and family members valuable time to perform preventive actions.
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Affiliation(s)
- Anup Kumar Mishra
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United States
- *Correspondence: Anup Kumar Mishra
| | - Marjorie Skubic
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United States
| | - Laurel A. Despins
- Sinclair School of Nursing, University of Missouri, Columbia, MO, United States
| | - Mihail Popescu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United States
- Department of Health Management and Informatics, University of Missouri, Columbia, MO, United States
| | - James Keller
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United States
| | - Marilyn Rantz
- Sinclair School of Nursing, University of Missouri, Columbia, MO, United States
| | - Carmen Abbott
- School of Health Professions, Physical Therapy, University of Missouri, Columbia, MO, United States
| | - Moein Enayati
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Shradha Shalini
- Department of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States
| | - Steve Miller
- Sinclair School of Nursing, University of Missouri, Columbia, MO, United States
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