Identification of important factors in an inpatient fall risk prediction model to improve the quality of care using EHR and electronic administrative data: A machine-learning approach.
Identification of important factors in an inpatient fall risk prediction model to improve the quality of care using EHR and electronic administrative data: A machine-learning approach.
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DOI：
10.1016/j.ijmedinf.2020.104272
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发表时间：
2020-11
影响因子：
4.9
通讯作者：
Lucero RJ
中科院分区：
文献类型：
--
作者：
Lindberg DS;Prosperi M;Bjarnadottir RI;Thomas J;Crane M;Chen Z;Shear K;Solberg LM;Snigurska UA;Wu Y;Xia Y;Lucero RJ
住院病人的福尔斯跌倒，许多导致受伤或死亡，是医院环境中的一个严重问题。现有的福尔斯风险评估工具，如莫尔斯Fall Scale，根据一组因素给出风险评分，但不一定表明哪些因素对预测福尔斯最重要。人工智能（AI）方法提供了一个提高预测性能的机会，同时还可以识别与医院获得性福尔斯相关的最重要的风险因素。我们可以通过将分类树、装袋、随机森林和自适应增强方法应用于电子健康记录（EHR）数据来深入了解这些风险因素。本研究的目的是使用基于树的机器学习方法来确定住院福尔斯最重要的预测因素，同时通过交叉验证来验证每个预测因素。使用EHR和2013年1月1日至2013年10月31日期间收集的14个内科外科单位的电子管理数据设计了一项病例对照研究。数据包含38个预测变量，包括患者特征、入院信息、评估信息、临床数据和组织特征。分类树，装袋，随机森林，自适应助推方法被用来确定最重要的因素，住院跌倒的风险，通过变量的重要性措施。通过十倍交叉验证计算灵敏度、特异性和ROC曲线下面积，并通过成对t检验进行比较。这些方法也进行了比较，单变量逻辑回归的莫尔斯跌倒量表总分。在AUROC方面，装袋（0.89），随机森林（0.90）和增强（0.89）都优于莫尔斯瀑布量表（0.86）和分类树（0.85），但在装袋，随机森林和自适应增强之间没有测量到差异，p值为0.05。福尔斯史、年龄、莫尔斯跌倒量表总分、步态质量、单元类型、精神状态和高跌倒风险增加药物（FRID）数量被认为是预测住院跌倒风险的最重要特征。机器学习方法有可能识别出最相关和最新的因素，用于检测有跌倒风险的住院患者，这将提高患者护理质量，并更全面地支持医疗保健提供者和组织领导决策。护士将能够提高他们的判断，以照顾病人的风险福尔斯。我们的研究也可以作为其他医源性疾病的基于AI的预测模型的开发的参考。据我们所知，这是第一项基于人工智能方法的使用报告患者，临床和组织特征重要性的研究。
Inpatient falls, many resulting in injury or death, are a serious problem in hospital settings. Existing falls risk assessment tools, such as the Morse Fall Scale, give a risk score based on a set of factors, but don’t necessarily signal which factors are most important for predicting falls. Artificial intelligence (AI) methods provide an opportunity to improve predictive performance while also identifying the most important risk factors associated with hospital-acquired falls. We can glean insight into these risk factors by applying classification tree, bagging, random forest, and adaptive boosting methods applied to Electronic Health Record (EHR) data. The purpose of this study was to use tree-based machine learning methods to determine the most important predictors of inpatient falls, while also validating each via cross-validation. A case-control study was designed using EHR and electronic administrative data collected between January 1, 2013 to October 31, 2013 in 14 medical surgical units. The data contained 38 predictor variables which comprised of patient characteristics, admission information, assessment information, clinical data, and organizational characteristics. Classification tree, bagging, random forest, and adaptive boosting methods were used to identify the most important factors of inpatient fall-risk through variable importance measures. Sensitivity, specificity, and area under the ROC curve were computed via ten-fold cross validation and compared via pairwise t-tests. These methods were also compared to a univariate logistic regression of the Morse Fall Scale total score. In terms of AUROC, bagging (0.89), random forest (0.90), and boosting (0.89) all outperformed the Morse Fall Scale (0.86) and the classification tree (0.85), but no differences were measured between bagging, random forest, and adaptive boosting, at a p-value of 0.05. History of Falls, Age, Morse Fall Scale total score, quality of gait, unit type, mental status, and number of high fall risk increasing drugs (FRIDs) were considered the most important features for predicting inpatient fall risk. Machine learning methods have the potential to identify the most relevant and novel factors for the detection of hospitalized patients at risk of falling, which would improve the quality of patient care, and to more fully support healthcare provider and organizational leadership decision-making. Nurses would be able to enhance their judgement to caring for patients at risk for falls. Our study may also serve as a reference for the development of AI-based prediction models of other iatrogenic conditions. To our knowledge, this is the first study to report the importance of patient, clinical, and organizational features based on the use of AI approaches.
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影响因子：
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