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CTBN-PH: A continuous-time Bayesian network for individualised diagnostic risk prediction.

Guillem Hernández Guillamet1, Francesc López Seguí2, Josep Vidal Alaball3

  • 1eXiT Research Group, Universitat de Girona (UdG), EPS - Edifici P-IV, Carrer Universitat de Girona, 6, Girona, 17003, Catalunya, Spain; Assistance strategy management. Hospital Germans Trias i Pujol, (ICS), Carretera de Canyet, Badalona, 08916, Catalunya, Spain; Research Group on Innovation, Health Economics and Digital Transformation, Institut Germans Trias i Pujol (IGTP), Cami de les Escoles, Badalona, 08916, Catalunya, Spain.

Computers in Biology and Medicine
|September 24, 2025
PubMed
Summary

This study introduces the CTBN-PH model, integrating Continuous-Time Bayesian Networks with Cox Proportional Hazards models for personalized disease trajectory prediction. The model effectively captures complex causal structures and individual patient risk factors in healthcare data.

Keywords:
Causal inferenceContinuous time bayesian networksCox proportional hazardsDisease modellingIndividualised risk

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Area of Science:

  • * Computational Biology
  • * Medical Informatics
  • * Biostatistics

Background:

  • * Continuous-time Bayesian networks (CTBNs) offer dynamic modeling of disease progression but often lack patient-specific predictions.
  • * Covariates significantly influence diagnostic transitions, posing a challenge for standard CTBN applications.
  • * Integrating covariate effects into CTBNs is crucial for individualizing disease trajectory predictions.

Purpose of the Study:

  • * To introduce the CTBN-PH model, combining CTBNs and Cox Proportional Hazards (Cox-PH) models.
  • * To enable individualized prediction of complex disease trajectories by incorporating covariate effects.
  • * To leverage causal topologies from healthcare data for dynamic risk estimation.

Main Methods:

  • * Integration of CTBNs with Cox-PH models to form the CTBN-PH model.
  • * Learning causal topologies from extensive healthcare trajectories (over 2.1 million patients).
  • * Dynamic adjustment of transition intensities based on covariate effects for personalized risk assessment.

Main Results:

  • * The CTBN-PH model successfully learned complex causal structures related to multi-morbid conditions like diabetes and hypertension.
  • * Achieved an Integrated Brier Score (IBS) of 0.153 for predicting single diagnosis onset over 25 years.
  • * Demonstrated strong performance in forecasting system inertia (IBS of 0.04 over four years) compared to non-individualized models.
  • * Validated utility in simulating patient trajectories tailored to specific covariate-defined populations.

Conclusions:

  • * The CTBN-PH model provides a robust framework for individualized disease trajectory prediction.
  • * Incorporating causal inference and covariate effects significantly enhances predictive accuracy.
  • * The model offers valuable applications in clinical decision support and personalized medicine simulations.