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Related Experiment Video

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EMR-based medical knowledge representation and inference via Markov random fields and distributed representation

Chao Zhao1, Jingchi Jiang1, Yi Guan1

  • 1School of Computer Science and Technology, Harbin, Heilongjiang 150001, China.

Artificial Intelligence in Medicine
|April 26, 2018
PubMed
Summary
This summary is machine-generated.

This study developed a system using electronic medical records (EMRs) to create an EMR-based medical knowledge network (EMKN) for clinical decision support (CDS). The system effectively supports tasks like diagnosis and treatment recommendations.

Keywords:
Clinical decision supportDistributed representationElectronic medical recordMarkov random fieldMedical knowledge network

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Knowledge Representation

Background:

  • Electronic medical records (EMRs) are rich sources of clinical knowledge.
  • Clinical decision support (CDS) systems can leverage EMR data for improved patient care.
  • Developing generalizable systems for EMR knowledge extraction is crucial.

Purpose of the Study:

  • To develop a general system for extracting and representing knowledge from EMRs.
  • To support three key CDS tasks: test recommendation, initial diagnosis, and treatment plan recommendation.
  • To infer patient conditions based on given symptoms and medical history.

Main Methods:

  • Constructed an EMR-based medical knowledge network (EMKN) from extracted medical entities.
  • Utilized three bipartite subgraphs (bigraphs) from the EMKN, modeled as Markov random fields (MRFs).
  • Proposed novel graph-based and likelihood-based energy functions, including two based on knowledge representation learning.

Main Results:

  • The proposed system demonstrated superior performance compared to baseline methods.
  • Distributed representations of medical entities effectively captured knowledge-level similarity.
  • Evaluation on two EMR datasets confirmed the system's efficacy.

Conclusions:

  • Combining EMKN and MRF provides an effective approach for medical knowledge representation and inference.
  • Task-specific energy functions are necessary for optimal performance in different CDS tasks.
  • The developed system offers a robust framework for leveraging EMR data for clinical decision support.