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首页> 外文期刊>BMC Medical Informatics and Decision Making >Modeling and prediction of pressure injury in hospitalized patients using artificial intelligence
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Modeling and prediction of pressure injury in hospitalized patients using artificial intelligence

机译：人工智能住院患者压力损伤的建模与预测

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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.
机译：医院收养的压力损伤（PIS）诱导显着的患者患有痛苦，膨胀医疗费用，并增加临床合作生命性。 PIS主要是由于床上不动，感官障碍，床位和住院时间的长度。在本研究中，我们使用电子健康记录和行政数据来使用人工智能（AI）来检查PI开发的贡献因素。我们使用高级数据科学技术首先预处理数据，然后培训机器学习分类器以预测开发PIS的概率。 AI培训基于住院患者的大型，不一致，不完整，异质，和时差数据。基于模型的统计方法和无模型AI策略用于预测PI结果并确定高度预测结果的突出特征。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。通过重新平衡培训数据以及模型学习阶段的布拉登，改善了所有AI方法的性能。与神经网络和线性建模相比，随着和不重新平衡或使用Braden评分，随机森林一致地产生最佳PI预测。 AI技术显示有望在不同外科服务中自动识别出现医院收购PIS风险的患者。我们的PI预测模型为开发PIS风险的常规患者提供了第一代AI指导。本研究为设计，实施和评估了解决特定医疗保健需求的新型干预措施提供了基础。具体而言，这种方法允许检查各种动态，个性化和临床环境影响对来自各种外科服务的医院患者的PI预防。

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