Prediction and decision-making based on nonlinear risks model in stomach cancer treatment
Objectives. The goals are to develop a nonlinear risk model and examine its prediction applicability for clinical use.Methods. Methods of survival analysis and regression statistical models were used.Results. A practical approach to assessing nonlinear risks of adverse events using the example of ga...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | Russian |
| Published: |
National Academy of Sciences of Belarus, the United Institute of Informatics Problems
2024-03-01
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| Series: | Informatika |
| Subjects: | |
| Online Access: | https://inf.grid.by/jour/article/view/1274 |
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| Summary: | Objectives. The goals are to develop a nonlinear risk model and examine its prediction applicability for clinical use.Methods. Methods of survival analysis and regression statistical models were used.Results. A practical approach to assessing nonlinear risks of adverse events using the example of gastric cancer treatment is proposed. A model for predicting the metachronous peritoneal dissemination in patients undergoing radical surgery for gastric cancer was proposed and studied. Assessment of risks for various periods of observation was performed, and the clinical suitability of developed approach was assessed.Conclusion. In clinical oncological practice, not only timely treatment plays an important role, but also the prevention of adverse outcomes after treatment. Individualization of patient monitoring after treatment reduces the risks of fatal outcomes and the costs of additional research and treatment in the event of cancer progression. Based on the results of this study, we propose solutions that should lead to more effective and high-quality treatment tactics and follow-up after treatment for gastric cancer, also to the selection of optimal approaches and to obtaining clinically favorable outcomes of the disease. The proposed risk prediction method will ultimately lead to individualized patient management based on the results of personal data. |
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| ISSN: | 1816-0301 |