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Effect of Nursing Assessment on Predictive Delirium Models in Hospitalised Patients

机译：护理评估对住院患者预测谵妄模型的影响

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Delirium is an acute neuropsychiatric syndrome which is common in elderly patients during their hospitalisation and is associated with an increased mortality and morbidity. Since delirium is a) often underdiagnosed and b) preventable if early signs are detected,igh expectations are set in delirium risk assessment during hospital admission. In our latest studies, we showed that delirium prediction using machine learning algorithms is possible based on the patients' health history. The aim of this study is to compare the influence of nursing assessment data on prediction models with clinical and demographic data. We approached the problem by a) comparing the performance of predictive models including nursing data with models based on clinical and demographic data only and b) analysing the feature importance of all available features. From our results we concluded that nursing assessment data can improve the performance of delirium prediction models better than demographic, laboratory, diagnosis, procedures, and previous transfers' data alone.
机译：谵妄是一种急性神经精神综合征，在住院期间，老年患者常见，并且与增加的死亡率和发病率增加有关。由于谵妄是a）通常是下调和b）如果检测到早期迹象，则可预防，在医院入学期间谵妄风险评估中预期。在我们最新的研究中，我们表明，基于患者的健康历史，可以使用机器学习算法使用机器学习算法的谵妄预测。本研究的目的是比较护理评估数据对具有临床和人口统计数据的预测模型的影响。我们通过走近问题）比较预测模型，其中仅基于和b临床和人口统计学数据），分析了所有可用的功能，该功能重要性与模型护理数据的表现。从我们的研究结果，我们得出的结论是护理评估数据可以更好地提高谵妄预测模型的性能比人口统计，实验室诊断，程序和以前的传输数据本身。

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