The feasibility of a Bayesian network model to assess the probability of simultaneous symptoms in patients with advanced cancer

Lotte van der Stap*, Myrthe F. van Haaften, Esther F. van Marrewijk, Albert H. de Heij, Paula L. Jansen, Janine M.N. Burgers, Melle S. Sieswerda, Renske K. Los, Anna K.L. Reyners, Yvette M. van der Linden

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
24 Downloads (Pure)


Although patients with advanced cancer often experience multiple symptoms simultaneously, clinicians usually focus on symptoms that are volunteered by patients during regular history-taking. We aimed to evaluate the feasibility of a Bayesian network (BN) model to predict the presence of simultaneous symptoms, based on the presence of other symptoms. Our goal is to help clinicians prioritize which symptoms to assess. Patient-reported severity of 11 symptoms (scale 0–10) was measured using an adapted Edmonton Symptom Assessment Scale (ESAS) in a national cross-sectional survey among advanced cancer patients. Scores were dichotomized (< 4 and ≥ 4). Using fourfold cross validation, the prediction error of 9 BN algorithms was estimated (Akaike information criterion (AIC). The model with the highest AIC was evaluated. Model predictive performance was assessed per symptom; an area under curve (AUC) of ≥ 0.65 was considered satisfactory. Model calibration compared predicted and observed probabilities; > 10% difference was considered inaccurate. Symptom scores of 532 patients were collected. A symptom score ≥ 4 was most prevalent for fatigue (64.7%). AUCs varied between 0.60 and 0.78, with satisfactory AUCs for 8/11 symptoms. Calibration was accurate for 101/110 predicted conditional probabilities. Whether a patient experienced fatigue was directly associated with experiencing 7 other symptoms. For example, in the absence or presence of fatigue, the model predicted a 8.6% and 33.1% probability of experiencing anxiety, respectively. It is feasible to use BN development for prioritizing symptom assessment. Fatigue seems most eligble to serve as a starting symptom for predicting the probability of experiencing simultaneous symptoms.

Original languageEnglish
Article number22295
JournalScientific Reports
Issue number1
Publication statusPublished - 24 Dec 2022

Bibliographical note

Funding Information:
This study was funded by a grant from ZonMw, The Netherlands Organisation for Health Research and Development (grant number 844001402).

Publisher Copyright: © 2022, The Author(s).


Dive into the research topics of 'The feasibility of a Bayesian network model to assess the probability of simultaneous symptoms in patients with advanced cancer'. Together they form a unique fingerprint.

Cite this