School of Nursing, University of Michigan, Ann Arbor, MI, 48109, USA.
Statistics Online Computational Resource (SOCR), University of Michigan, Ann Arbor, MI, 48109, USA.
BMC Med Inform Decis Mak. 2021 Aug 30;21(1):253. doi: 10.1186/s12911-021-01608-5.
Hospital-acquired pressure injuries (PIs) induce significant patient suffering, inflate healthcare costs, and increase clinical co-morbidities. PIs are mostly due to bed-immobility, sensory impairment, bed positioning, and length of hospital stay. In this study, we use electronic health records and administrative data to examine the contributing factors to PI development using artificial intelligence (AI).
We used advanced data science techniques to first preprocess the data and then train machine learning classifiers to predict the probability of developing PIs. The AI training was based on large, incongruent, incomplete, heterogeneous, and time-varying data of hospitalized patients. Both model-based statistical methods and model-free AI strategies were used to forecast PI outcomes and determine the salient features that are highly predictive of the outcomes.
Our findings reveal that PI prediction by model-free techniques outperform model-based forecasts. The performance of all AI methods is improved by rebalancing the training data and by including the Braden in the model learning phase. Compared to neural networks and linear modeling, with and without rebalancing or using Braden scores, Random forest consistently generated the optimal PI forecasts.
AI techniques show promise to automatically identify patients at risk for hospital acquired PIs in different surgical services. Our PI prediction model provide a first generation of AI guidance to prescreen patients at risk for developing PIs.
This study provides a foundation for designing, implementing, and assessing novel interventions addressing specific healthcare needs. Specifically, this approach allows examining the impact of various dynamic, personalized, and clinical-environment effects on PI prevention for hospital patients receiving care from various surgical services.
医院获得性压疮（PI）会给患者带来严重的痛苦，增加医疗保健成本，并增加临床合并症。PI 主要是由于卧床不动、感觉障碍、床位定位和住院时间延长引起的。在这项研究中，我们使用电子健康记录和管理数据，使用人工智能（AI）检查 PI 发展的促成因素。
我们使用先进的数据科学技术首先对数据进行预处理，然后训练机器学习分类器来预测 PI 的发生概率。AI 训练基于住院患者的大量、不一致、不完整、异构和时变数据。我们使用基于模型的统计方法和无模型 AI 策略来预测 PI 结果，并确定对结果具有高度预测性的显著特征。
我们的研究结果表明，无模型技术的 PI 预测优于基于模型的预测。通过重新平衡训练数据和在模型学习阶段纳入 Braden，所有 AI 方法的性能都得到了提高。与神经网络和线性建模相比，无论是否重新平衡或使用 Braden 评分，随机森林始终生成最佳的 PI 预测结果。
AI 技术有望自动识别不同外科服务中发生医院获得性 PI 的风险患者。我们的 PI 预测模型为筛选有发生 PI 风险的患者提供了第一代 AI 指导。
这项研究为设计、实施和评估针对特定医疗需求的新型干预措施提供了基础。具体来说，这种方法允许检查各种动态、个性化和临床环境对接受来自各种外科服务的护理的医院患者的 PI 预防的影响。
