R C Ambagtsheer1, N Shafiabady2, E Dent3, C Seiboth4, J Beilby5. 1. Torrens University Australia, Adelaide, Australia; Fra. National Health and Medical Research Council of Australia Centre of Research Excellence Ilty Trans-Disciplinary Research to Achieve Healthy Ageing, Adelaide, Australia. Electronic address: rambagtsheer@laureate.net.au. 2. Torrens University Australia, Adelaide, Australia. 3. Torrens University Australia, Adelaide, Australia; Baker Heart and Diabetes Institute, Melbourne, Australia; Lutheran Services, Milton, Brisbane, Australia. 4. Lutheran Services, Milton, Brisbane, Australia. 5. Torrens University Australia, Adelaide, Australia; Fra. National Health and Medical Research Council of Australia Centre of Research Excellence Ilty Trans-Disciplinary Research to Achieve Healthy Ageing, Adelaide, Australia.
Abstract
INTRODUCTION: Research has shown that frailty, a geriatric syndrome associated with an increased risk of negative outcomes for older people, is highly prevalent among residents of residential aged care facilities (also called long term care facilities or nursing homes). However, progress on effective identification of frailty within residential care remains at an early stage, necessitating the development of new methods for accurate and efficient screening. OBJECTIVES: We aimed to determine the effectiveness of artificial intelligence (AI) algorithms in accurately identifying frailty among residents aged 75 years and over in comparison with a calculated electronic Frailty Index (eFI) based on a routinely-collected residential aged care administrative data set drawn from 10 residential care facilities located in Queensland, Australia. A secondary objective included the identification of best-performing candidate algorithms. METHODS: We designed a frailty prediction system based on the eFI identification of frailty, allocating 84.5 % and 15.5 % of the data to training and test data sets respectively. We compared the performance of 18 specific scenarios to predict frailty against eFI based on unique combinations of three ML algorithms (support vector machines [SVM], decision trees [DT] and K-nearest neighbours [KNN]) and six cases (6, 10, 11, 14, 39 and 70 input variables). We calculated accuracy, percentage positive and negative agreement, sensitivity, specificity, Cohen's kappa and Prevalence- and Bias- Adjusted Kappa (PABAK), table frequencies and positive and negative predictive values. RESULTS: Of 592 eligible resident records, 500 were allocated to the training set and 92 to the test set. Three scenarios (10, 11 and 70 input variables), all based on SVM algorithm, returned overall accuracy above 75 %. CONCLUSIONS: There is some potential for AI techniques to contribute towards better frailty identification within residential care. However, potential benefits will need to be weighed against administrative burden, data quality concerns and presence of potential bias.
INTRODUCTION: Research has shown that frailty, a geriatric syndrome associated with an increased risk of negative outcomes for older people, is highly prevalent among residents of residential aged care facilities (also called long term care facilities or nursing homes). However, progress on effective identification of frailty within residential care remains at an early stage, necessitating the development of new methods for accurate and efficient screening. OBJECTIVES: We aimed to determine the effectiveness of artificial intelligence (AI) algorithms in accurately identifying frailty among residents aged 75 years and over in comparison with a calculated electronic Frailty Index (eFI) based on a routinely-collected residential aged care administrative data set drawn from 10 residential care facilities located in Queensland, Australia. A secondary objective included the identification of best-performing candidate algorithms. METHODS: We designed a frailty prediction system based on the eFI identification of frailty, allocating 84.5 % and 15.5 % of the data to training and test data sets respectively. We compared the performance of 18 specific scenarios to predict frailty against eFI based on unique combinations of three ML algorithms (support vector machines [SVM], decision trees [DT] and K-nearest neighbours [KNN]) and six cases (6, 10, 11, 14, 39 and 70 input variables). We calculated accuracy, percentage positive and negative agreement, sensitivity, specificity, Cohen's kappa and Prevalence- and Bias- Adjusted Kappa (PABAK), table frequencies and positive and negative predictive values. RESULTS: Of 592 eligible resident records, 500 were allocated to the training set and 92 to the test set. Three scenarios (10, 11 and 70 input variables), all based on SVM algorithm, returned overall accuracy above 75 %. CONCLUSIONS: There is some potential for AI techniques to contribute towards better frailty identification within residential care. However, potential benefits will need to be weighed against administrative burden, data quality concerns and presence of potential bias.
Authors: Sylvia Aponte-Hao; Sabrina T Wong; Manpreet Thandi; Paul Ronksley; Kerry McBrien; Joon Lee; Mathew Grandy; Dee Mangin; Alan Katz; Alexander Singer; Donna Manca; Tyler Williamson Journal: Int J Popul Data Sci Date: 2021-09-10
Authors: Kathrin Seibert; Dominik Domhoff; Dominik Bruch; Matthias Schulte-Althoff; Daniel Fürstenau; Felix Biessmann; Karin Wolf-Ostermann Journal: J Med Internet Res Date: 2021-11-29 Impact factor: 5.428
Authors: Reshma Aziz Merchant; Richard Jor Yeong Hui; Sing Cheer Kwek; Meena Sundram; Arthur Tay; Jerome Jayasundram; Matthew Zhixuan Chen; Shu Ee Ng; Li Feng Tan; John E Morley Journal: Front Med (Lausanne) Date: 2020-07-08