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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Medical Concept Representation Learning from Multi-source Data.

Tian Bai1, Brian L Egleston2, Richard Bleicher2

  • 1Department of Computer and Information Sciences, Temple University, USA.

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Summary
This summary is machine-generated.

This study introduces a novel method to represent medical codes from various sources in a unified vector space. This approach improves the accuracy of identifying similar medical codes across different classification systems.

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Area of Science:

  • Computational linguistics
  • Medical informatics
  • Machine learning

Background:

  • Vector representations of words are crucial for natural language processing.
  • This concept has been applied to medical codes in claims for analysis and prediction.
  • Challenges arise from multi-source medical claims using diverse or combined ontologies.

Purpose of the Study:

  • To develop a method for representing medical codes from different ontologies within a single vector space.
  • To enable effective utilization of multi-source medical claim data.
  • To improve cross-ontology code referencing.

Main Methods:

  • Modified the Pointwise Mutual Information (PMI) measure for code similarity.
  • Developed a novel negative sampling method for the word2vec model.
  • Implicitly factorized the modified PMI matrix.

Main Results:

  • Evaluated the approach on the medical code cross-reference problem.
  • Tested cross-referencing between ICD-9 and CPT medical code ontologies.
  • Achieved superior cross-referencing performance compared to existing methods.

Conclusions:

  • The proposed vector representation method effectively handles multi-source medical claim data.
  • This approach enhances the ability to identify similar codes across different medical ontologies.
  • The method offers a significant improvement for exploratory analysis and predictive modeling in healthcare.