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A Causal Discovery Workflow for Rare Diseases: Experts-in-the-Loop Analysis of Sparse Longitudinal Data.

Niccolò Rocchi1,2, Alessio Zanga3,4, Alice Bernasconi1,2

  • 1Department of Informatics, Systems and Communication, Università degli Studi di Milano - Bicocca, Viale Sarca 336, Milan, 20126, Italy.

Journal of Medical Systems
|January 16, 2026
PubMed
Summary

This study introduces an expert-in-the-loop workflow for causal discovery in rare diseases. It generates causal networks to understand disease mechanisms and improve personalized clinical decision-making.

Keywords:
Causal discoveryLongitudinal dataPrior knowledgeRare diseasesSoft tissue sarcoma

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Area of Science:

  • Computational biology
  • Medical informatics
  • Causal inference

Background:

  • Causal networks offer mechanistic insights for personalized medicine.
  • Causal discovery is difficult for rare diseases due to sparse data and incomplete knowledge.
  • Disease progression over time adds complexity to causal modeling.

Purpose of the Study:

  • To develop an expert-in-the-loop causal discovery workflow.
  • To iteratively refine causal networks representing disease mechanisms.
  • To create a comprehensive causal model for rare diseases like soft tissue sarcoma.

Main Methods:

  • An expert-in-the-loop causal discovery workflow was proposed.
  • The workflow iteratively refines causal networks.
  • Applied to soft tissue sarcoma to model its natural history.

Main Results:

  • The workflow generated three causal networks for soft tissue sarcoma.
  • These networks describe the interplay between patient covariates and disease behavior.
  • This represents the first comprehensive causal description of the disease's natural history.

Conclusions:

  • The proposed workflow enhances causal discovery for rare, complex diseases.
  • It enables personalized treatment strategies through improved clinical decision-making.
  • The method is agile, modular, and flexible for data-sparse, longitudinal clinical domains.