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Robust and efficient semi-supervised learning for Ising model.

Daiqing Wu1, Molei Liu2

  • 1Department of Statistical Sciences, University of Toronto, Toronto, ON M5S 1A1, Canada.

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Summary
This summary is machine-generated.

This study introduces a novel semi-supervised learning (SSL) method to efficiently infer Ising models for analyzing multiple disease interactions using electronic health records (EHRs). The approach improves learning efficiency with limited labeled data by leveraging unlabeled EHR data.

Keywords:
EHR surrogateIsing modelintrinsic efficiencyscore functionsemi-supervised learning

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Area of Science:

  • Biomedical Informatics
  • Statistical Modeling
  • Machine Learning

Background:

  • Characterizing interactive modes of multiple disease outcomes is crucial in biomedical studies.
  • Ising models are commonly used but face efficiency challenges with scarce labeled data, especially from electronic health records (EHRs).

Purpose of the Study:

  • To develop a novel semi-supervised learning (SSL) method for efficient Ising model inference.
  • To address the data scarcity problem in learning Ising models from EHR data.

Main Methods:

  • Developed a novel SSL method by modeling outcomes against auxiliary EHR features.
  • Projected the score function of a supervised estimator onto EHR features and incorporated unlabeled data for variance reduction.
  • Proposed strategies for conditional modeling leveraging EHR information with moderate complexity.
  • Introduced efficient updates and ensemble methods to mitigate potential misspecification issues.

Main Results:

  • The proposed SSL method demonstrated improved efficiency and performance compared to existing SSL approaches in simulation studies.
  • Asymptotic theory justified the method's validity.
  • The method's utility was illustrated on real-world data concerning intensive care unit (ICU) admission phenotypes from the MIMIC-III dataset.

Conclusions:

  • The novel SSL method offers an efficient solution for Ising model inference in biomedical studies with limited labeled EHR data.
  • The approach effectively utilizes auxiliary EHR features and unlabeled data to enhance learning accuracy and reduce variance without bias.