Abstract
Chronic lymphocytic leukemia (CLL) is the most common blood cancer in adults. The course of CLL and patients' response to treatment are varied. This variability makes it difficult to select the most appropriate treatment regimen and predict the progression of the disease. This work was aimed at developing and validating dynamic Bayesian networks (DBNs) to predict changes of the health status of patients with CLL and progression of the disease over time. Two DBNs were developed and implemented i.e. Health Status Network (HSN) and Treatment Effect Network (TEN). Based on the literature data and expert knowledge we identified relationships linking the most important factors influencing the health status and treatment effects in patients with CLL. The developed networks, and in particular TEN, were able to predict probability of survival in patients with CLL, which was in line with the survival data collected in large medical registries. The networks can be used to personalize the predictions, taking into account a priori knowledge concerning a particular patient with CLL. The proposed approach can serve as a basis for the development of artificial intelligence systems that facilitate the choice of treatment that maximizes the chances of survival in patients with CLL.
Highlights
Chronic lymphocytic leukemia (CLL) is the most common blood cancer in adults
Due to complexity of the problem, the first dynamic Bayesian networks (DBNs) that we developed, which was called Health Status Network (HSN), was aimed at predicting the health status of patients with CLL, assuming that each patient was treated in accordance with the best medical practice, reflected in the results presented in the available medical literature
The first network—HSN allows predicting, among others, probability of survival, death due to infections, death from other cancers, death from causes not related to CLL and death from transformation of CLL into a more aggressive form of leukemia, in both, the patient described by average values of all the parameters included in the network when no a priori knowledge about a particular patient is available as well as the patient in whom the values of some parameters are known, i.e. when a priori knowledge about the patient can be used to adjust probabilities in conditional probability tables (CPTs) of some nodes in the network
Summary
Chronic lymphocytic leukemia (CLL) is the most common blood cancer in adults. The course of CLL and patients’ response to treatment are varied. This variability makes it difficult to select the most appropriate treatment regimen and predict the progression of the disease. This work was aimed at developing and validating dynamic Bayesian networks (DBNs) to predict changes of the health status of patients with CLL and progression of the disease over time. Chronic lymphocytic leukemia (CLL) is a non-Hodgkin lymphoma and the most common blood cancer in adults[1]. The incidence of CLL increases with age It is rarely diagnosed in people under the age of 40 years. More than 30% of patients with CLL never require treatment and die from causes other than CLL
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