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Related Concept Videos

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Hierarchical Information Criterion for Variable Abstraction.

Mark Mirtchouk1, Bharat Srikishan1, Samantha Kleinberg1

  • 1Computer Science, Stevens Institute of Technology, Hoboken, NJ, USA.

Proceedings of Machine Learning Research
|January 24, 2022
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Summary
This summary is machine-generated.

We introduce a Hierarchical Information Criterion (HIC) to automatically select the best granularity for variables in large biomedical datasets. This machine learning approach improves prediction accuracy for critical health outcomes.

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Area of Science:

  • Biomedical informatics
  • Machine learning
  • Data science

Background:

  • Large biomedical datasets pose challenges for machine learning due to numerous variables.
  • Existing feature selection methods do not leverage hierarchical data structures (e.g., ICD-9 codes, gene ontologies).
  • Researchers must manually determine the optimal level of granularity for hierarchical variables, which is time-consuming and suboptimal.

Purpose of the Study:

  • To develop a novel method for automated variable abstraction and selection in hierarchical biomedical data.
  • To address the limitation of manual granularity selection in feature engineering.
  • To improve the performance of machine learning models by effectively utilizing hierarchical information.

Main Methods:

  • Proposed a novel Hierarchical Information Criterion (HIC) based on mutual information.
  • Developed a method for automated abstraction and ranking of hierarchical features.
  • Applied HIC to feature selection for mortality prediction tasks using MIMIC-III ICU data.

Main Results:

  • HIC significantly improved performance, achieving an average AUROC increase of 0.053 over traditional methods.
  • The proposed method outperformed existing state-of-the-art approaches, increasing AUROC from 0.819 to 0.887.
  • Demonstrated the effectiveness of automated hierarchical feature selection in biomedical prediction tasks.

Conclusions:

  • The Hierarchical Information Criterion (HIC) offers a powerful, automated solution for feature selection in hierarchical biomedical data.
  • This approach enhances machine learning model performance by optimizing variable granularity.
  • HIC represents a significant advancement in leveraging complex, structured data for improved clinical predictions.