Bayesian Networks for Cause Effect Modeling (600 Patients)

  • Ton J. Cleophas
  • Aeilko H. Zwinderman


Bayesian networks are probabilistic graphical models using nodes and arrows, respectively representing variables, and probabilistic dependencies between two variables. Computations in a Bayesian network are performed using weighted likelihood methodology and marginalization, meaning that irrelevant variables are integrated or summed out. Additional theoretical information is given in Machine Learning in medicine part two, Chap. 16, Bayesian networks, pp. 163–170, Springer Heidelberg Germany, 2013, from the same authors). This chapter is to assess if Bayesian networks is able to determine direct and indirect predictors of binary outcomes like morbidity/mortality outcomes.

Supplementary material

333106_2_En_75_MOESM1_ESM.csv (11 kb)
longevity (CSV 11 kb)
333106_2_En_75_MOESM2_ESM.sav (5 kb)
longevity (SAV 4 kb)

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ton J. Cleophas
    • 1
  • Aeilko H. Zwinderman
    • 2
  1. 1.Department Medicine Albert Schweitzer HospitalDordrechtThe Netherlands
  2. 2.Academic Medical CenterDepartment Biostatistics and EpidemiologyAmsterdamThe Netherlands

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