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Real-world data medical knowledge graph: construction and applications.

Linfeng Li1, Peng Wang2, Jun Yan3

  • 1Institute of Information Science, Beijing Jiaotong University, Beijing, China; Yidu Cloud Technology Inc., Beijing, China.

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|March 8, 2020
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Summary
This summary is machine-generated.

This study presents a systematic method to build medical knowledge graphs (KGs) from electronic medical records (EMRs). The novel approach enhances data representation and ranking, improving KG quality for intelligent healthcare applications.

Keywords:
CDSSPSRmedical knowledge graphquadrupletreal-world data

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence in Healthcare
  • Data Science

Background:

  • Medical knowledge graphs (KGs) are crucial for intelligent healthcare applications.
  • Existing methods for KG construction from electronic medical records (EMRs) require systematic improvements.
  • The need for high-quality, structured medical data is increasing.

Purpose of the Study:

  • To introduce a systematic approach for constructing a medical KG from large-scale EMRs.
  • To propose a novel quadruplet structure for medical knowledge representation.
  • To develop and evaluate a new related-entity ranking function (PSR).

Main Methods:

  • A systematic 8-step procedure for KG construction from de-identified EMRs.
  • Implementation of a novel quadruplet structure for knowledge representation.
  • Development of a Probability, Specificity, and Reliability (PSR) ranking function and use of the PrTransH algorithm for graph embedding.

Main Results:

  • A medical KG with 22,508 entities and 579,094 quadruplets was established.
  • The PSR ranking function improved Normalized Discounted Cumulative Gain (NDCG@10) from 0.799 to 0.906 compared to TF/IDF.
  • Disease clustering validated the effectiveness of learned entity and relation embeddings.

Conclusions:

  • The systematic procedure efficiently constructs high-quality medical KGs from large EMR datasets.
  • The PSR ranking function significantly outperforms existing methods.
  • The learned embeddings effectively represent semantic information, enabling successful KG applications.