Aging population in many developed countries, moves the issue of healthy aging at the forefront of the political, scientific and technological concerns. Delirium is a multifactorial disorder that is highly prevalent in hospitalized elderly people that causes complications in the patient care and increases mortality at the hospital and soon after discharge. Early diagnostics would allow improved treatment and prevention for a syndrome that requires very personalized treatment. This paper deals with machine learning based prediction of delirium at hospital admittance as a computer aided diagnostic tool, as well as with the identification of risk factors by means of the variable importance computed by the classifier model building approaches. We achieve almost 0.80 classification accuracy, which is encourages further exploration of improved classifier models. Exploration of variable importance shows that frailty, dementia and some pharmacological factors are relevant risk factors for delirium at hospital admittance.

中文翻译：

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入院时谵妄预测的危险因素

在许多发达国家，人口老龄化，将健康老龄化问题推到了政治、科技关注的前沿。谵妄是一种多因素疾病，在住院的老年人中非常普遍，会导致患者护理出现并发症并增加医院和出院后不久的死亡率。早期诊断可以改善对需要非常个性化治疗的综合征的治疗和预防。本文处理基于机器学习的入院谵妄预测作为计算机辅助诊断工具，以及通过分类器模型构建方法计算的变量重要性识别风险因素。我们达到了几乎 0.80 的分类准确率，这鼓励了进一步探索改进的分类器模型。对变量重要性的探索表明，虚弱、痴呆和一些药理因素是入院时谵妄的相关危险因素。