Bionic models for identification of biological systems; Journal of Physics: Conference Series; Vol. 803 : Information Technologies in Business and Industry (ITBI2016)
| Parent link: | Journal of Physics: Conference Series Vol. 803 : Information Technologies in Business and Industry (ITBI2016).— 2017.— [012046, 6 p.] |
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| Summary: | Title screen This article proposes a clinical decision support system that processes biomedical data. For this purpose a bionic model has been designed based on neural networks, genetic algorithms and immune systems. The developed system has been tested on data from pregnant women. The paper focuses on the approach to enable selection of control actions that can minimize the risk of adverse outcome. The control actions (hyperparameters of a new type) are further used as an additional input signal. Its values are defined by a hyperparameter optimization method. A software developed with Python is briefly described. |
| Idioma: | inglés |
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2017
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| Acceso en liña: | http://dx.doi.org/10.1088/1742-6596/803/1/012046 http://earchive.tpu.ru/handle/11683/38147 |
| Formato: | Electrónico Capítulo de libro |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=654335 |
| Summary: | Title screen This article proposes a clinical decision support system that processes biomedical data. For this purpose a bionic model has been designed based on neural networks, genetic algorithms and immune systems. The developed system has been tested on data from pregnant women. The paper focuses on the approach to enable selection of control actions that can minimize the risk of adverse outcome. The control actions (hyperparameters of a new type) are further used as an additional input signal. Its values are defined by a hyperparameter optimization method. A software developed with Python is briefly described. |
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| DOI: | 10.1088/1742-6596/803/1/012046 |