Dynamic Bayesian networks for prediction of health status and treatment effect in patients with chronic lymphocytic leukemia
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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-05813-8 |
| _version_ | 1798023794923667456 |
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| author | Piotr Ladyzynski Maria Molik Piotr Foltynski |
| author_facet | Piotr Ladyzynski Maria Molik Piotr Foltynski |
| author_sort | Piotr Ladyzynski |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T17:52:12Z |
| format | Article |
| id | doaj.art-81b85cfc11c54d728bff4d7974daa0fc |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T17:52:12Z |
| publishDate | 2022-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-81b85cfc11c54d728bff4d7974daa0fc2022-12-22T04:11:01ZengNature PortfolioScientific Reports2045-23222022-02-0112111410.1038/s41598-022-05813-8Dynamic Bayesian networks for prediction of health status and treatment effect in patients with chronic lymphocytic leukemiaPiotr Ladyzynski0Maria Molik1Piotr Foltynski2Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of SciencesNalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of SciencesNalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of SciencesAbstract 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.https://doi.org/10.1038/s41598-022-05813-8 |
| spellingShingle | Piotr Ladyzynski Maria Molik Piotr Foltynski Dynamic Bayesian networks for prediction of health status and treatment effect in patients with chronic lymphocytic leukemia Scientific Reports |
| title | Dynamic Bayesian networks for prediction of health status and treatment effect in patients with chronic lymphocytic leukemia |
| title_full | Dynamic Bayesian networks for prediction of health status and treatment effect in patients with chronic lymphocytic leukemia |
| title_fullStr | Dynamic Bayesian networks for prediction of health status and treatment effect in patients with chronic lymphocytic leukemia |
| title_full_unstemmed | Dynamic Bayesian networks for prediction of health status and treatment effect in patients with chronic lymphocytic leukemia |
| title_short | Dynamic Bayesian networks for prediction of health status and treatment effect in patients with chronic lymphocytic leukemia |
| title_sort | dynamic bayesian networks for prediction of health status and treatment effect in patients with chronic lymphocytic leukemia |
| url | https://doi.org/10.1038/s41598-022-05813-8 |
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