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首页> 外文会议>IEEE International Conference on Healthcare Informatics >Variable Selection for Chronic Disease Outcome Prediction Using a Causal Inference Technique: A Preliminary Study
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Variable Selection for Chronic Disease Outcome Prediction Using a Causal Inference Technique: A Preliminary Study

机译：使用因果推断技术进行慢性疾病结果预测的变量选择：初步研究

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摘要

The ability to predict health outcomes of patients with chronic conditions has the potential for early risk factor identification, better treatment planning, and shared decision making. Compared to prediction tasks for acute conditions, modeling chronic diseases require careful adjustment for time-dependencies among treatments and responses, as well as variable selection to identify significant predictors. In this paper, targeting outcome prediction for chronic conditions which often require multiple medications, we applied causal inference techniques, specifically, the g-computation formula and marginal structural model, for the purpose of input variable selection prior to prediction using Bayesian networks. We propose that this approach allows for interpretable variable selection that also leads to better outcome prediction. An evaluation was performed using electronic health record data of a cohort of chronic kidney disease (CKD) patients to predict CKD progression. We identified effects of individual and concurrently used drugs on patients' kidney functions that are different across CKD stages. Lastly, using proposed variation selection technique, we predicted CKD progression with accuracy as high as 0.74, slightly outperforming logistic regression.
机译：预测慢性病患者健康结局的能力具有潜在的早期危险因素识别，更好的治疗计划和共同决策的潜力。与针对急性病的预测任务相比，对慢性病进行建模需要针对治疗和反应之间的时间依赖性以及对变量的选择进行仔细调整，以识别出重要的预测因素。在本文中，针对经常需要多种药物治疗的慢性病的结果预测，我们应用了因果推理技术，特别是g运算公式和边际结构模型，目的是在使用贝叶斯网络进行预测之前选择输入变量。我们建议这种方法允许可解释的变量选择，这也导致更好的结果预测。使用一组慢性肾脏病（CKD）患者的电子健康记录数据进行了评估，以预测CKD的进展。我们确定了单独和同时使用的药物对患者肾脏功能的影响，这些影响在整个CKD阶段均不同。最后，使用拟议的变异选择技术，我们预测了CKD的进展，其准确性高达0.74，略好于logistic回归。

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