PDF(Pubmed)
Abstract：
OBJECTIVE: to describe the development of a predictive nursing workload classifier model, using artificial intelligence.
METHODS: retrospective observational study, using secondary sources of electronic patient records, using machine learning. The convenience sample consisted of 43,871 assessments carried out by clinical nurses using the Perroca Patient Classification System, which served as the gold standard, and clinical data from the electronic medical records of 11,774 patients, which constituted the variables. In order to organize the data and carry out the analysis, the Dataiku® data science platform was used. Data analysis occurred in an exploratory, descriptive and predictive manner. The study was approved by the Ethics and Research Committee of the institution where the study was carried out.
RESULTS: the use of artificial intelligence enabled the development of the nursing workload assessment classifier model, identifying the variables that most contributed to its prediction. The algorithm correctly classified 72% of the variables and the area under the Receiver Operating Characteristic curve was 82%.
CONCLUSIONS: a predictive model was developed, demonstrating that it is possible to train algorithms with data from the patient\'s electronic medical record to predict the nursing workload and that artificial intelligence tools can be effective in automating this activity.
METHODS: retrospective observational study, using secondary sources of electronic patient records, using machine learning. The convenience sample consisted of 43,871 assessments carried out by clinical nurses using the Perroca Patient Classification System, which served as the gold standard, and clinical data from the electronic medical records of 11,774 patients, which constituted the variables. In order to organize the data and carry out the analysis, the Dataiku® data science platform was used. Data analysis occurred in an exploratory, descriptive and predictive manner. The study was approved by the Ethics and Research Committee of the institution where the study was carried out.
RESULTS: the use of artificial intelligence enabled the development of the nursing workload assessment classifier model, identifying the variables that most contributed to its prediction. The algorithm correctly classified 72% of the variables and the area under the Receiver Operating Characteristic curve was 82%.
CONCLUSIONS: a predictive model was developed, demonstrating that it is possible to train algorithms with data from the patient\'s electronic medical record to predict the nursing workload and that artificial intelligence tools can be effective in automating this activity.
摘要：
目的：描述预测护理工作量分类器模型的开发，使用人工智能。
方法：回顾性观察研究，使用电子病历的次要来源，使用机器学习。便利样本包括由临床护士使用Perroca患者分类系统进行的43,871项评估，作为黄金标准，以及来自11,774名患者的电子病历的临床数据，构成变量。为了组织数据并进行分析，使用Dataiku®数据科学平台。数据分析发生在探索性的，描述性和预测性的方式。该研究得到了进行研究的机构的伦理和研究委员会的批准。
结果：使用人工智能实现了护理工作量评估分类器模型的开发，确定对其预测贡献最大的变量。该算法正确地分类了72%的变量,并且接收器工作特性曲线下的面积为82%。
结论：建立了一个预测模型，证明有可能使用患者电子病历中的数据训练算法来预测护理工作量，并且人工智能工具可以有效地自动化此活动。
方法：回顾性观察研究，使用电子病历的次要来源，使用机器学习。便利样本包括由临床护士使用Perroca患者分类系统进行的43,871项评估，作为黄金标准，以及来自11,774名患者的电子病历的临床数据，构成变量。为了组织数据并进行分析，使用Dataiku®数据科学平台。数据分析发生在探索性的，描述性和预测性的方式。该研究得到了进行研究的机构的伦理和研究委员会的批准。
结果：使用人工智能实现了护理工作量评估分类器模型的开发，确定对其预测贡献最大的变量。该算法正确地分类了72%的变量,并且接收器工作特性曲线下的面积为82%。
结论：建立了一个预测模型，证明有可能使用患者电子病历中的数据训练算法来预测护理工作量，并且人工智能工具可以有效地自动化此活动。
