Zhou Nina, Brook Robert D, Dinov Ivo D, Wang Lu
Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
Statistics Online Computational Resource, University of Michigan, Ann Arbor, MI, USA.
Biom J. 2022 Apr;64(4):805-817. doi: 10.1002/bimj.202100077. Epub 2022 Feb 3.
The wide-scale adoption of electronic health records (EHRs) provides extensive information to support precision medicine and personalized health care. In addition to structured EHRs, we leverage free-text clinical information extraction (IE) techniques to estimate optimal dynamic treatment regimes (DTRs), a sequence of decision rules that dictate how to individualize treatments to patients based on treatment and covariate history. The proposed IE of patient characteristics closely resembles "The clinical Text Analysis and Knowledge Extraction System" and employs named entity recognition, boundary detection, and negation annotation. It also utilizes regular expressions to extract numerical information. Combining the proposed IE with optimal DTR estimation, we extract derived patient characteristics and use tree-based reinforcement learning (T-RL) to estimate multistage optimal DTRs. IE significantly improved the estimation in counterfactual outcome models compared to using structured EHR data alone, which often include incomplete data, data entry errors, and other potentially unobserved risk factors. Moreover, including IE in optimal DTR estimation provides larger study cohorts and a broader pool of candidate tailoring variables. We demonstrate the performance of our proposed method via simulations and an application using clinical records to guide blood pressure control treatments among critically ill patients with severe acute hypertension. This joint estimation approach improves the accuracy of identifying the optimal treatment sequence by 14-24% compared to traditional inference without using IE, based on our simulations over various scenarios. In the blood pressure control application, we successfully extracted significant blood pressure predictors that are unobserved or partially missing from structured EHR.
电子健康记录（EHRs）的广泛采用提供了大量信息，以支持精准医学和个性化医疗保健。除了结构化的电子健康记录外，我们还利用自由文本临床信息提取（IE）技术来估计最佳动态治疗方案（DTRs），这是一系列决策规则，规定了如何根据治疗和协变量历史为患者个性化定制治疗方案。所提出的患者特征信息提取方法与“临床文本分析与知识提取系统”非常相似，采用了命名实体识别、边界检测和否定标注。它还利用正则表达式来提取数值信息。将所提出的信息提取方法与最佳动态治疗方案估计相结合，我们提取出派生的患者特征，并使用基于树的强化学习（T-RL）来估计多阶段最佳动态治疗方案。与仅使用结构化电子健康记录数据相比，信息提取在反事实结果模型中的估计有显著改善，结构化电子健康记录数据往往包含不完整的数据、数据录入错误以及其他潜在的未观察到的风险因素。此外，在最佳动态治疗方案估计中纳入信息提取可提供更大的研究队列和更广泛的候选定制变量池。我们通过模拟以及使用临床记录指导重症急性高血压危重症患者血压控制治疗的应用来证明我们所提出方法的性能。基于我们在各种场景下的模拟，与不使用信息提取的传统推理相比，这种联合估计方法将识别最佳治疗序列的准确性提高了14%-24%。在血压控制应用中，我们成功提取了结构化电子健康记录中未观察到或部分缺失的重要血压预测因素。
