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A Data-Driven Approach to Quantifying Immune States in Sepsis
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Missing data imputation on biomedical data using deeply learned clustering and L2 regularized regression based on

Gayathri Nagarajan1, L D Dhinesh Babu1

  • 1School of Information Technology and Engineering, VIT university, India.

Artificial Intelligence in Medicine
|January 9, 2022
PubMed
Summary

This study introduces a novel imputation method for missing biomedical data, improving accuracy and efficiency. The approach enhances classification tasks by preserving dataset structure, crucial for high-dimensional genomic data.

Keywords:
Biomedical datasetsDeeply learned clusteringL2 regularizationMissing data imputation

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Area of Science:

  • Biomedical Informatics
  • Data Science
  • Genomics

Background:

  • The proliferation of high-dimensional biomedical data, including genomic datasets and electronic health records, presents challenges in handling missing values.
  • Incomplete data can lead to inaccurate analyses and misleading conclusions, necessitating robust imputation techniques.
  • Existing imputation methods often struggle with high-dimensional data, balancing imputation accuracy with computational efficiency and data structure preservation.

Purpose of the Study:

  • To propose a novel missing data imputation approach for biomedical datasets.
  • To address the limitations of existing methods in terms of imputation accuracy, computational efficiency, and preservation of dataset structure, particularly for high-dimensional genomic data.
  • To evaluate the proposed method's performance against established and recent imputation techniques.

Main Methods:

  • An ensemble approach combining deep learning-based clustering and L2 regularized regression with symmetric uncertainty was developed.
  • Experiments were conducted on both genomic and non-genomic biomedical datasets with varying proportions and patterns of missing data.
  • The proposed method was rigorously compared against seven baseline imputation methods and two recent approaches.

Main Results:

  • The novel imputation approach demonstrated superior performance over all compared methods in terms of imputation accuracy.
  • The method achieved significant improvements in computational efficiency, a critical factor for high-dimensional datasets.
  • Preservation of the dataset's inherent structure was maintained, leading to enhanced overall classification accuracy in biomedical tasks.

Conclusions:

  • The proposed ensemble imputation technique offers a computationally efficient and accurate solution for missing data in biomedical datasets.
  • This method effectively handles high-dimensional data, including genomic information, by preserving its structure.
  • The improved imputation accuracy and efficiency translate to better performance in downstream biomedical classification tasks.