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An Analytical Method for Multimorbidity Management Using Bayesian Networks.

Stéphane Deparis1, Alessandra Pascale1, Pierpaolo Tommasi1

  • 1IBM Research - Ireland, Dublin, Ireland.

Studies in Health Technology and Informatics
|April 22, 2018
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Summary

Bayesian Networks model multimorbidity using vital signs and lifestyle data. This approach aids managing chronic conditions with wearable sensors and digital tools.

Keywords:
Bayesian networkTILDAUIcategorical predictionmultimorbidity

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Area of Science:

  • Computational biology
  • Health informatics
  • Artificial intelligence in medicine

Background:

  • Multimorbidity presents complex health management challenges.
  • Wearable sensors and digital tools offer potential for improved patient self-management.
  • Existing models may not fully capture the dynamic interplay of factors in multimorbidity.

Purpose of the Study:

  • To develop and evaluate a Bayesian Network model for persons with multimorbidity.
  • To integrate vital signs and lifestyle data for predictive health insights.
  • To support daily condition management for individuals with multimorbidity and their caregivers.

Main Methods:

  • Utilized a score-based approach to learn a categorical Bayesian Network structure.
  • Employed constraints on variable ordering for model development.
  • Assessed prediction accuracy using the Brier score via cross-validation.
  • Leveraged data from the TILDA longitudinal health study of the older Irish population.

Main Results:

  • A functional Bayesian Network model was successfully developed.
  • The model demonstrated predictive accuracy assessed through cross-validation.
  • A user interface was created for interactive querying of model probabilities.

Conclusions:

  • Bayesian Networks offer a viable framework for modeling multimorbidity.
  • The developed model can aid in understanding and managing complex health conditions.
  • Integration of sensor data and user interfaces enhances patient empowerment.