Supermodeling in predictive diagnostics of cancer under treatment
| Autor: | Witold Dzwinel, Adrian Kłusek, Leszek Siwik |
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| Rok vydání: | 2021 |
| Předmět: |
Scheme (programming language)
Computer science business.industry media_common.quotation_subject Synchronizing Health Informatics Models Theoretical Machine learning computer.software_genre Computer Science Applications Data assimilation Neoplasms Fitting algorithm Key (cryptography) Humans Quality (business) Artificial intelligence business Baseline (configuration management) computer Algorithms Abstraction (linguistics) media_common computer.programming_language |
| Zdroj: | Computers in biology and medicine. 137 |
| ISSN: | 1879-0534 |
| Popis: | Classical data assimilation (DA) techniques, synchronizing a computer model with observations, are highly demanding computationally, particularly, for complex over-parametrized cancer models. Consequently, current models are not sufficiently flexible to interactively explore various therapy strategies, and to become a key tool of predictive oncology. We show that, by using supermodeling, it is possible to develop a prediction/correction scheme that could attain the required time regimes and be directly used to support decision-making in anticancer therapies. A supermodel is an interconnected ensemble of individual models (sub-models); in this case, the variously parametrized baseline tumor models. The sub-model connection weights are trained from data, thereby incorporating the advantages of the individual models. Simultaneously, by optimizing the strengths of the connections, the sub-models tend to partially synchronize with one another. As a result, during the evolution of the supermodel, the systematic errors of the individual models partially cancel each other. We find that supermodeling allows for a radical increase in the accuracy and efficiency of data assimilation. We demonstrate that it can be considered as a meta-procedure for any classical parameter fitting algorithm, thus it represents the next – latent – level of abstraction of data assimilation. We conclude that supermodeling is a very promising paradigm that can considerably increase the quality of prognosis in predictive oncology. |
| Databáze: | OpenAIRE |
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