Department of Biomedical Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Department of Artificial Intelligence in Healthcare, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
J Biomed Inform. 2021 Mar;115:103692. doi: 10.1016/j.jbi.2021.103692. Epub 2021 Feb 4.
The goal of this work was to capture diseases in patients by comprehending the fine-grained medical conditions and disease progression manifested by transitions in medical conditions. We realize this by introducing our earlier work on a state-of-the-art knowledge presentation, which defines a disease as a causal chain of abnormal states (CCAS). Here, we propose a framework, EHR2CCAS, for constructing a system to map electronic health record (EHR) data to CCAS.
EHR2CCAS is a framework consisting of modules that access heterogeneous EHR to estimate the presence of abnormal states in a CCAS for a patient in a given time window. EHR2CCAS applies expert-driven (rule-based) and data-driven (machine learning) methods to identify abnormal states from structured and unstructured EHR data. It features data-driven approaches for unlocking clinical texts and imputations based on the EHR temporal properties and the causal CCAS structure. This study presents the CCAS of chronic kidney disease as an example. A mapping system between the EHR from the University of Tokyo Hospital and CCAS of chronic kidney disease was constructed and evaluated against expert annotation.
The system achieved high prediction performance in identifying abnormal states that had strong agreement among annotators. Our handling of narrative varieties in texts and our imputation of the presence of an abnormal state markedly improved the prediction performance. EHR2CCAS presents patient data describing the temporal presence of abnormal states in CCAS, which is useful in individual disease progression management. Further analysis of the differentiation of transition among abnormal states outputted by EHR2CCAS can contribute to detecting disease subtypes.
This work represents the first step toward combining disease knowledge and EHR to extract abnormality related to a disease defined as fine-grained abnormal states and transitions among them. This can aid in disease progression management and deep phenotyping.
本研究旨在通过理解由医疗状况转变所体现的细微医学病症和疾病进展，从而捕捉患者的疾病。我们通过引入早期关于先进知识呈现的工作来实现这一点，该工作将疾病定义为异常状态的因果链（CCAS）。在这里，我们提出了一个框架 EHR2CCAS，用于构建将电子健康记录（EHR）数据映射到 CCAS 的系统。
EHR2CCAS 是一个由模块组成的框架，这些模块访问异构的 EHR，以估算给定时间窗口内患者 CCAS 中异常状态的存在情况。EHR2CCAS 应用专家驱动（基于规则）和数据驱动（机器学习）方法从结构化和非结构化的 EHR 数据中识别异常状态。它具有基于 EHR 时间特性和因果 CCAS 结构解锁临床文本和插补的方法。本研究以慢性肾脏病的 CCAS 为例。构建了东京大学医院 EHR 与慢性肾脏病 CCAS 之间的映射系统，并针对专家注释进行了评估。
该系统在识别具有强烈注释者一致性的异常状态方面具有出色的预测性能。我们对文本中叙述多样性的处理和异常状态存在的插补明显提高了预测性能。EHR2CCAS 呈现出描述 CCAS 中异常状态时间存在的患者数据，这对于个体疾病进展管理非常有用。进一步分析 EHR2CCAS 输出的异常状态之间转换的差异可以有助于发现疾病亚型。
这项工作代表了将疾病知识与 EHR 结合起来，以提取与疾病相关的异常的第一步，这些异常被定义为细微的异常状态和它们之间的转变。这有助于疾病进展管理和深度表型分析。
