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Cardiology record multi-label classification using latent Dirichlet allocation.

Jorge Pérez1, Alicia Pérez2, Arantza Casillas1

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This study introduces an efficient framework using Latent Dirichlet Allocation (LDA) topic modeling to analyze electronic health records (EHRs), enabling better exploration of patient data and disease associations for improved clinical insights.

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Area of Science:

  • Computational medicine
  • Health informatics
  • Machine learning for healthcare

Background:

  • Electronic health records (EHRs) contain valuable clinical knowledge but are challenging to analyze due to their volume and complexity.
  • Exploring EHRs for patient-specific insights and disease co-occurrence patterns, such as heart disease interventions, requires efficient analytical tools.
  • Current methods face challenges in handling lengthy records and multi-label classification settings.

Purpose of the Study:

  • To develop an efficient framework for exploring EHRs, offering alternative views of patient segments and disease associations.
  • To enable clustering and statistical analysis of disease development, including co-occurrences with other conditions.
  • To address the challenge of analyzing large datasets with numerous classes in a multi-label context.

Main Methods:

  • Latent Dirichlet Allocation (LDA) topic modeling was employed to represent EHRs as distributions of latent topics and words.
  • Topic models were evaluated using divergence metrics and applied to multi-label classification of EHRs using ICD-10 codes.
  • The study focused on cardiology-related EHRs, with an average of 7 ICD-10 codes assigned from a set of 970.

Main Results:

  • Topic models demonstrated discriminative ability, with latent topics linked to ICD-10 codes for interpretability.
  • LDA provided a computationally efficient, low-dimensional representation of EHRs, outperforming symbolic approaches like TF-IDF.
  • Supervised classifiers inferred from LDA representations achieved an average Area Under the Curve (AUC) of 77.79.

Conclusions:

  • Topic modeling offers a compact, continuous space representation of EHRs, effectively conveying relevant information through hidden topics.
  • This approach facilitates the extraction of International Classification of Diseases 10th Clinical Modification (ICD-10) codes from EHRs.
  • The developed framework and associated software (Python, R) enhance the exploration and analysis of clinical data.