The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set 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 routinelycollected residential aged care administrative data set drawn from 10 residential care facilities located in Queensland, Australia. A secondary objective included the identification of bestperforming 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 Knearest neighbours KNN) and six cases (6, 10, 11, 14, 39 and 70 input variables). We calculated accuracy, percentage positive and negative agreement, sensitivity, specificity, Cohenxe2x80x99s 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.

Building a Risk Model for the Patientcentred Care of Multiple Chronic Diseases With the increase of multimorbidity due to population ageing, managing multiple chronic health conditions is a rising challenge. Machinelearning can contribute to a better understanding of persons with multimorbidity (PwMs) and how to design an effective framework of care and support for them. We present a risk model of older PwMs that was derived from the TILDA dataset, a longitudinal study of the ageing Irish population. This model is based on a 26nodes Bayesian network that represents patients possibly having one or more chronic conditions among diabetes, chronic obstructive pulmonary disease and arthritis, through a joint probability distribution of demographic, symptomatic and behavioral dimensions. We describe our method, give an exploratory analysis of the risk model, and assess its prediction accuracy in a crossvalidation experiment. Finally we discuss its use in supporting management of care for PwMs, drawing on comments from health practitioners on the model.