Data_Sheet_1_Unifying Diagnosis Identification and Prediction Method Embedding the Disease Ontology Structure From Electronic Medical Records.ZIP

ObjectiveThe reasonable classification of a large number of distinct diagnosis codes can clarify patient diagnostic information and help clinicians to improve their ability to assign and target treatment for primary diseases. Our objective is to identify and predict a unifying diagnosis (UD) from electronic medical records (EMRs).MethodsWe screened 4,418 sepsis patients from a public MIMIC-III database and extracted their diagnostic information for UD identification, their demographic information, laboratory examination information, chief complaint, and history of present illness information for UD prediction. We proposed a data-driven UD identification and prediction method (UDIPM) embedding the disease ontology structure. First, we designed a set similarity measure method embedding the disease ontology structure to generate a patient similarity matrix. Second, we applied affinity propagation clustering to divide patients into different clusters, and extracted a typical diagnosis code co-occurrence pattern from each cluster. Furthermore, we identified a UD by fusing visual analysis and a conditional co-occurrence matrix. Finally, we trained five classifiers in combination with feature fusion and feature selection method to unify the diagnosis prediction.ResultsThe experimental results on a public electronic medical record dataset showed that the UDIPM could extracted a typical diagnosis code co-occurrence pattern effectively, identified and predicted a UD based on patients' diagnostic and admission information, and outperformed other fusion methods overall.ConclusionsThe accurate identification and prediction of the UD from a large number of distinct diagnosis codes and multi-source heterogeneous patient admission information in EMRs can provide a data-driven approach to assist better coding integration of diagnosis.

目标：对大量独特的诊断代码进行合理分类，旨在阐明患者诊断信息，并有助于临床医生提升对主要疾病的治疗分配和针对性。本研究的目的是从电子病历（EMR）中识别和预测统一诊断（UD）。方法：我们从公共MIMIC-III数据库中筛选出4,418例脓毒症患者的数据，并提取其用于UD识别的诊断信息，以及用于UD预测的人口统计学信息、实验室检查信息、主诉和现病史信息。我们提出了一种数据驱动的UD识别和预测方法（UDIPM），该方法嵌入疾病本体结构。首先，我们设计了一种嵌入疾病本体结构的集合相似度度量方法，以生成患者相似度矩阵。其次，我们应用亲和传播聚类将患者划分为不同的簇，并从每个簇中提取典型的诊断代码共现模式。进一步地，通过融合视觉分析和条件共现矩阵，我们识别了UD。最后，我们结合特征融合和特征选择方法训练了五个分类器以实现诊断预测的统一。结果：在公共电子病历数据集上的实验结果表明，UDIPM能够有效地提取典型的诊断代码共现模式，基于患者的诊断和入院信息识别和预测UD，并且在整体上优于其他融合方法。结论：从大量独特的诊断代码和多源异构的EMR患者入院信息中准确识别和预测UD，可以提供一种数据驱动的方法，以辅助更好地实现诊断编码的整合。