Colorectal cancer BRB model optimization method and device based on parameter constraint condition
The invention discloses a colorectal cancer BRB model optimization method and device based on parameter constraint conditions. A complex nonlinear relation between case input symptoms and diagnosis disease types is analyzed in combination with clinical diagnosis experience of colorectal cancer medical experts, and a BRB system is established. Then, under the co-driving of expert knowledge and historical data, designing a constraint condition which is more complete for the confidence coefficient of the disease type, constructing an optimization model, and carrying out constraint optimization on parameters in the BRB through GA (Genetic Algorithm); according to the method, the constraint conditions are designed by combining expert knowledge and historical data, so that the accuracy of diagnosing the colorectal cancer by the BRB model is effectively improved..
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Patent| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Europäisches Patentamt - (2023) vom: 03. Okt. Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
LIU KEZHOU [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
Sonstige Themen: |
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| Anmerkungen: |
Source: www.epo.org (no modifications made), First posted: 2023-10-03, Last update posted on www.tib.eu: 2023-12-19, Last updated: 2023-12-22 |
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| Patentnummer: |
CN116844712 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
EPA018823572 |
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| 245 | 1 | 0 | |a Colorectal cancer BRB model optimization method and device based on parameter constraint condition |
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| 520 | |a The invention discloses a colorectal cancer BRB model optimization method and device based on parameter constraint conditions. A complex nonlinear relation between case input symptoms and diagnosis disease types is analyzed in combination with clinical diagnosis experience of colorectal cancer medical experts, and a BRB system is established. Then, under the co-driving of expert knowledge and historical data, designing a constraint condition which is more complete for the confidence coefficient of the disease type, constructing an optimization model, and carrying out constraint optimization on parameters in the BRB through GA (Genetic Algorithm); according to the method, the constraint conditions are designed by combining expert knowledge and historical data, so that the accuracy of diagnosing the colorectal cancer by the BRB model is effectively improved. | ||
| 650 | 4 | |a G06N: Computer systems based on specific computational models | |
| 650 | 4 | |a G16H: Healthcare informatics, i.e. information and communication technology [ict] specially adapted for the handling or processing of medical or healthcare data | |
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