Explainable Fall Risk Prediction in Older Adults Using Gait and Geriatric Assessments
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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Digital Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2022.869812/full |
| _version_ | 1818030343403339776 |
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| author | Anup Kumar Mishra Marjorie Skubic Laurel A. Despins Mihail Popescu Mihail Popescu James Keller Marilyn Rantz Carmen Abbott Moein Enayati Shradha Shalini Steve Miller |
| author_facet | Anup Kumar Mishra Marjorie Skubic Laurel A. Despins Mihail Popescu Mihail Popescu James Keller Marilyn Rantz Carmen Abbott Moein Enayati Shradha Shalini Steve Miller |
| author_sort | Anup Kumar Mishra |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T05:34:04Z |
| format | Article |
| id | doaj.art-c2aefbb311024cbe8718d0ab29f9cfe9 |
| institution | Directory Open Access Journal |
| issn | 2673-253X |
| language | English |
| last_indexed | 2024-12-10T05:34:04Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Digital Health |
| spelling | doaj.art-c2aefbb311024cbe8718d0ab29f9cfe92022-12-22T02:00:27ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2022-05-01410.3389/fdgth.2022.869812869812Explainable Fall Risk Prediction in Older Adults Using Gait and Geriatric AssessmentsAnup Kumar Mishra0Marjorie Skubic1Laurel A. Despins2Mihail Popescu3Mihail Popescu4James Keller5Marilyn Rantz6Carmen Abbott7Moein Enayati8Shradha Shalini9Steve Miller10Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United StatesDepartment of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United StatesSinclair School of Nursing, University of Missouri, Columbia, MO, United StatesDepartment of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United StatesDepartment of Health Management and Informatics, University of Missouri, Columbia, MO, United StatesDepartment of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United StatesSinclair School of Nursing, University of Missouri, Columbia, MO, United StatesSchool of Health Professions, Physical Therapy, University of Missouri, Columbia, MO, United StatesDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, United StatesDepartment of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United StatesSinclair School of Nursing, University of Missouri, Columbia, MO, United StatesOlder 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.https://www.frontiersin.org/articles/10.3389/fdgth.2022.869812/fullfall riskolder adultsexplainable AIgeriatric assessmentsgaitGAITRite |
| spellingShingle | Anup Kumar Mishra Marjorie Skubic Laurel A. Despins Mihail Popescu Mihail Popescu James Keller Marilyn Rantz Carmen Abbott Moein Enayati Shradha Shalini Steve Miller Explainable Fall Risk Prediction in Older Adults Using Gait and Geriatric Assessments Frontiers in Digital Health fall risk older adults explainable AI geriatric assessments gait GAITRite |
| title | Explainable Fall Risk Prediction in Older Adults Using Gait and Geriatric Assessments |
| title_full | Explainable Fall Risk Prediction in Older Adults Using Gait and Geriatric Assessments |
| title_fullStr | Explainable Fall Risk Prediction in Older Adults Using Gait and Geriatric Assessments |
| title_full_unstemmed | Explainable Fall Risk Prediction in Older Adults Using Gait and Geriatric Assessments |
| title_short | Explainable Fall Risk Prediction in Older Adults Using Gait and Geriatric Assessments |
| title_sort | explainable fall risk prediction in older adults using gait and geriatric assessments |
| topic | fall risk older adults explainable AI geriatric assessments gait GAITRite |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2022.869812/full |
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