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

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.

Predicting Lung Cancer Survival Using Probabilistic Reclassification of TNM Editions With a Bayesian Network.

Purpose: The TNM classification system is used for prognosis, treatment, and research. Regular updates potentially break backward compatibility. Reclassification is not always possible, is labor intensive, or requires additional data.

Impact on Quality of Documentation and Workload of the Introduction of a National Information Standard for Tumor Board Reporting.

Purpose: Tumor boards, clinical practice guidelines, and cancer registries are intertwined cancer care quality instruments. Standardized structured reporting has been proposed as a solution to improve clinical documentation, while facilitating data reuse for secondary purposes. This study describes the implementation and evaluation of a national standard for tumor board reporting for breast cancer on the basis of the clinical practice guideline and the potential for reusing clinical data for the Netherlands Cancer Registry (NCR).