Automatic Diagnosis With Efficient Medical Case Searching Based on Evolving Graphs
Automatic Diagnosis With Efficient Medical Case Searching Based on Evolving Graphs
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DOI：
10.1109/access.2018.2871769
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发表时间：
2018
期刊：
影响因子：
3.9
通讯作者：
Xiaoli Wang;Y. Wang;Chuchu Gao;Kunhui Lin;Yadi Li
中科院分区：
文献类型：
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作者：
Xiaoli Wang;Y. Wang;Chuchu Gao;Kunhui Lin;Yadi Li
临床数据通常是多模态的，包括结构化数据和非结构化数据。临床数据的建模已经成为医疗大数据分析中一个非常重要和具有挑战性的问题。大多数现有的系统只关注一种类型的数据。在本文中，我们提出了一种基于知识图的方法来建立各种类型的多模态数据之间的联系。首先，我们建立一个语义丰富的知识库，使用医学词典和实际的临床数据收集从医院。其次，我们提出了一种图建模方法来弥合不同类型的数据之间的差距，每个病人的多模态临床数据融合和建模为一个统一的轮廓图。为了捕获患者的临床病例的时间演变，轮廓图被表示为演变图的序列。第三，我们开发了一种基于图相似性搜索的自动诊断惰性学习算法。为了评估我们的方法，我们进行了实验研究，ICU病人的诊断和骨科病人分类。实验结果表明，该方法的性能优于基线算法。我们还实现了一个供临床使用的真实的自动诊断系统。从医院获得的结果显示了高精度。
The clinical data are often multimodal and consist of both structured data and unstructured data. The modeling of clinical data has become a very important and challenging problem in healthcare big data analytics. Most existing systems focus on only one type of data. In this paper, we propose a knowledge graph-based method to build the linkage between various types of multimodal data. First, we build a semantic-rich knowledge base using both medical dictionaries and practical clinical data collected from hospitals. Second, we propose a graph modeling method to bridge the gap between different types of data, and the multimodal clinical data of each patient are fused and modeled as one unified profile graph. To capture the temporal evolution of the patient’s clinical case, the profile graph is represented as a sequence of evolving graphs. Third, we develop a lazy learning algorithm for automatic diagnosis based on graph similarity search. To evaluate our method, we conduct experimental studies on ICU patient diagnosis and Orthopaedics patient classification. The results show that our method could outperform the baseline algorithms. We also implement a real automatic diagnosis system for clinical use. The results obtained from the hospital demonstrate high precision.