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The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set
استفاده از تکنیک های هوش مصنوعی برای شناسایی ضعف در یک مجموعه داده های اداری مراقبت از سالمندان مسکونی-2020 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. Keywords: Artificial intelligence| Frailty | Residential facilities | Machine learning | Health records | Personal |
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