Predicting health outcomes from high‐dimensional longitudinal health histories using relational random forests
Predicting health outcomes from high‐dimensional longitudinal health histories using relational random forests
复制标题
使用关系随机森林从高维纵向健康史预测健康结果
DOI：
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发表时间：
2015
影响因子：
1.3
通讯作者：
D. Madigan
中科院分区：
文献类型：
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作者：
Z. Shahn;P. Ryan;D. Madigan
随着电子健康记录 (EHR) 的兴起，人们对根据健康史预测健康结果的算法越来越感兴趣。然而，将健康史中包含的所有潜在相关信息转换为传统机器学习算法可管理的格式具有挑战性。具体来说，需要合并多个健康事件之间的相对时间关系，但由于此类预测变量的空间巨大，这项任务变得很困难。为此，我们修改并重新利用了最初在语音识别背景下开发的预测建模方法（其余称为随机关系森林，或 RRF）。 RRF 生成信息丰富的标记图，表示随机决策树节点处健康事件之间的时间关系。我们通过预测既往心房颤动 (afib) 患者的中风来说明 RRF 的实用性，并证明相对于更传统的预测模型具有潜在临床意义的改进。
With the rise of Electronic Health Records (EHRs), there is increasing interest in algorithms that predict health outcomes from health histories. However, it is challenging to translate all the potentially relevant information contained in a health history into a format that would be manageable for traditional machine learning algorithms. Specifically, it would be desirable to incorporate relative temporal relationships among multiple health events, a task made difficult by the vastness of the space of such predictors. To this end, we modify and repurpose a predictive modeling method (referred to in the remainder as Random Relational Forest, or RRF) originally developed in the context of speech recognition. RRF generates informative labeled graphs representing temporal relations among health events at the nodes of randomized decision trees. We illustrate the utility of RRF by predicting strokes in patients with prior diagnoses of Atrial Fibrillation (afib) and demonstrate potentially clinically meaningful improvements over more traditional predictive models.