Risk factors for prediction of delirium at hospital admittance
Risk factors for prediction of delirium at hospital admittance
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DOI：
10.1111/exsy.12698
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发表时间：
2021-04-01
期刊：
影响因子：
3.3
通讯作者：
Besga, Ariadna
中科院分区：
文献类型：
--
作者：
Cano-Escalera, Guillermo;Grana, Manuel;Besga, Ariadna
在许多发达国家，人口老龄化使健康老龄化问题成为政治、科学和技术关注的前沿问题。谵妄是一种多因素疾病，在住院老年人中非常普遍，在患者护理中引起并发症，并增加住院和出院后不久的死亡率。早期诊断可以改善这种需要个性化治疗的综合征的治疗和预防。本文讨论了基于机器学习的谵妄入院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来识别危险因素。我们实现了接近0.80的分类精度，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、痴呆和一些药理学因素是入院时谵妄的相关危险因素。
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.