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首页> 外文会议>IEEE International symposium on computer-based medical systems >Using Probabilistic Graphical Models to Enhance the Prognosis of Health-Related Quality of Life in Adult Survivors of Critical Illness
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Using Probabilistic Graphical Models to Enhance the Prognosis of Health-Related Quality of Life in Adult Survivors of Critical Illness

机译：使用概率图形模型增强重症成年幸存者健康相关生活质量的预后

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Health-related quality of life (HR-QoL) is a subjective concept, reflecting the overall mental and physical state of the patient, and their own sense of well-being. Estimating current and future QoL has become a major outcome in the evaluation of critically ill patients. The aim of this study is to enhance the inference process of 6 weeks and 6 months prognosis of QoL after intensive care unit (ICU) stay, using the EQ-5D questionnaire. The main outcomes of the study were the EQ-5D five main dimensions: mobility, self-care, usual activities, pain and anxiety depression. For each outcome, three Bayesian classifiers were built and validated with 10-fold cross-validation. Sixty and 473 patients (6 weeks and 6 months, respectively) were included. Overall, 6 months QoL is higher than 6 weeks, with the probability of absence of problems ranging from 31% (6 weeks mobility) to 72% (6 months self-care). Bayesian models achieved prognosis accuracies of 56% (6 months, anxiety depression) up to 80% (6 weeks, mobility). The prognosis inference process for an individual patient was enhanced with the visual analysis of the models, showing that women, elderly, or people with longer ICU stay have higher risk of QoL problems at 6 weeks. Likewise, for the 6 months prognosis, a higher APACHE II severity score also leads to a higher risk of problems, except for anxiety depression where the youngest and active have increased risk. Bayesian networks are competitive with less descriptive strategies, improve the inference process by incorporating domain knowledge and present a more interpretable model. The relationships among different factors extracted by the Bayesian models are in accordance with those collected by previous state-of-the-art literature, hence showing their usability as inference model.
机译：与健康相关的生活质量（HR-QoL）是一个主观的概念，反映了患者的整体心理和身体状况以及他们自己的幸福感。评估当前和未来的生活质量已成为评估重症患者的主要结果。这项研究的目的是使用EQ-5D问卷增强重症监护病房（ICU）住院后6周和6个月QoL预后的推断过程。该研究的主要结果是EQ-5D的五个主要方面：活动能力，自我护理，日常活动，疼痛和焦虑抑郁。对于每个结果，构建了三个贝叶斯分类器，并通过10倍交叉验证进行了验证。包括60名和473名患者（分别为6周和6个月）。总体而言，6个月的生活质量高于6周，没有问题的可能性范围从31％（6周活动时间）到72％（6个月自我护理）。贝叶斯模型的预后准确性达到56％（6个月，焦虑抑郁）至80％（6周，活动性）。模型的可视化分析增强了单个患者的预后推断过程，表明女性，老年人或ICU住院时间较长的人在6周时有较高的QoL问题风险。同样，对于6个月的预后，较高的APACHE II严重性评分也会导致出现问题的风险更高，除了焦虑抑郁症（最年轻和活跃的人都具有更高的风险）。贝叶斯网络具有较少描述性策略的竞争能力，通过合并领域知识来改善推理过程并提供更具可解释性的模型。贝叶斯模型提取的不同因素之间的关系与以前的最新文献所收集的一致，因此显示了它们作为推理模型的可用性。

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