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Related Experiment Video

Updated: Aug 9, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Enhancing Clinical Data Analysis by Explaining Interaction Effects between Covariates in Deep Neural Network Models.

Yijun Shao1,2, Ali Ahmed1,2,3, Edward Y Zamrini1,2,4,5,6

  • 1Department of Clinical Research and Leadership, School of Medicine and Health Sciences, George Washington University, Washington, DC 20037, USA.

Journal of Personalized Medicine
|February 25, 2023
PubMed
Summary
This summary is machine-generated.

We developed a novel "interaction score" method to measure covariate interactions in deep neural networks (DNNs) and other machine learning models. This interpretable score aids in understanding complex disease risk prediction, including for Alzheimer's disease and related dementias.

Keywords:
Alzheimer’s disease and related dementiadeep learningrisk analysis

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Area of Science:

  • Machine Learning
  • Biostatistics
  • Computational Biology

Background:

  • Deep neural networks (DNNs) are increasingly used for disease risk prediction.
  • DNNs excel at modeling complex, non-linear relationships, including covariate interactions.
  • Existing methods for quantifying these interactions in DNNs are limited.

Purpose of the Study:

  • To introduce a novel, interpretable method called "interaction scores" for measuring covariate interactions in DNNs.
  • To demonstrate the model-agnostic nature of interaction scores, allowing application to various machine learning models.
  • To validate the method's performance using simulated and real-world clinical data.

Main Methods:

  • Developed "interaction scores" as a generalization of logistic regression interaction terms.
  • Calculated interaction scores at both individual and population levels for comprehensive analysis.
  • Applied the method to simulated datasets and a clinical dataset for Alzheimer's disease and related dementias (ADRD).
  • Compared interaction scores with two existing interaction measurement methods.

Main Results:

  • Interaction scores effectively captured underlying interaction effects in simulated data.
  • Strong correlations were observed between population-level scores and ground truth values.
  • Individual-level scores demonstrated variability consistent with non-uniform interactions.
  • Analysis of ADRD data revealed both known and novel covariate interactions.

Conclusions:

  • The interaction score method provides an interpretable and effective way to quantify covariate interactions in machine learning models.
  • This method enhances the understanding of complex relationships in disease risk prediction.
  • Interaction scores offer valuable insights for both individual patient assessment and population-level studies, as demonstrated in ADRD research.