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.

中文翻译：

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人工智能（AI）技术在住宅养老服务管理数据集中识别脆弱性的应用。

引言研究表明，衰弱是一种老年人综合症，与老年人负面结果的风险增加相关，在居住老年护理机构（也称为长期护理机构或疗养院）的居民中非常普遍。然而，在住宅护理中有效识别脆弱性的进展仍处于早期阶段，因此需要开发新的方法以进行准确而有效的筛查。目标我们旨在确定人工智能（AI）算法在准确识别75岁及以上居民中的脆弱性方面的有效性，并与根据常规收集的居民老年护理管理数据集计算得出的电子脆弱性指数（eFI）进行比较位于澳大利亚昆士兰州的10个住宅护理设施。第二个目标是确定性能最佳的候选算法。方法我们基于eFI脆弱性识别设计了脆弱性预测系统，分别将84.5％和15.5％的数据分配给训练和测试数据集。我们基于三种ML算法（支持向量机[SVM]，决策树[DT]和近邻K [KNN]）的独特组合以及六种情况（6，10，10）比较了18种特定情况的性能，以预测对eFI的脆弱性，11、14、39和70个输入变量）。我们计算了准确性，阳性和阴性一致性百分比，敏感性，特异性，科恩卡伯值和患病率和偏差调整后的κ（PABAK），表格频率以及正负预测值。结果592个符合条件的居民记录中，500个分配给训练集，而92个分配给测试集。全部基于SVM算法的三个场景（10、11和70个输入变量）返回的总体准确率超过75％。结论AI技术有可能有助于更好地识别住宅护理中的脆弱性。但是，需要权衡潜在收益与管理负担，数据质量问题和存在潜在偏见。