Medical missing data interpolation method based on table learning
The invention belongs to the technical field of medical data processing, and particularly relates to a medical missing data interpolation method based on table learning. The method comprises the following steps: acquiring medical missing data to be interpolated and preprocessing the medical missing data to obtain preprocessed medical missing data; performing pre-interpolation and conversion operation on the preprocessed medical missing data to obtain preliminary complete medical data and a missing mask matrix; processing the preliminary complete medical data and the missing mask matrix by adopting an improved generator to obtain corrected complete medical data; inputting the missing mask matrix into a prompt generator to obtain a prompt matrix; processing the corrected medical data and the prompt matrix by adopting an improved discriminator to obtain an estimated mask matrix; calculating model loss and adjusting model parameters according to the model loss to obtain a trained medical missing data interpolation model; the method is high in interpolation precision and high in interpolation speed..
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Patent| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Europäisches Patentamt - (2023) vom: 20. Okt. Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
HU FENG [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Anmerkungen: |
Source: www.epo.org (no modifications made), First posted: 2023-10-20, Last update posted on www.tib.eu: 2024-01-22, Last updated: 2024-01-26 |
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| Patentnummer: |
CN116913445 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
EPA018922619 |
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| 245 | 1 | 0 | |a Medical missing data interpolation method based on table learning |
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| 500 | |a Source: www.epo.org (no modifications made), First posted: 2023-10-20, Last update posted on www.tib.eu: 2024-01-22, Last updated: 2024-01-26 | ||
| 520 | |a The invention belongs to the technical field of medical data processing, and particularly relates to a medical missing data interpolation method based on table learning. The method comprises the following steps: acquiring medical missing data to be interpolated and preprocessing the medical missing data to obtain preprocessed medical missing data; performing pre-interpolation and conversion operation on the preprocessed medical missing data to obtain preliminary complete medical data and a missing mask matrix; processing the preliminary complete medical data and the missing mask matrix by adopting an improved generator to obtain corrected complete medical data; inputting the missing mask matrix into a prompt generator to obtain a prompt matrix; processing the corrected medical data and the prompt matrix by adopting an improved discriminator to obtain an estimated mask matrix; calculating model loss and adjusting model parameters according to the model loss to obtain a trained medical missing data interpolation model; the method is high in interpolation precision and high in interpolation speed. | ||
| 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 | |
| 650 | 4 | |a inf | |
| 650 | 4 | |a G06F: Electric digital data processing (computer systems based on specific computational models g06n) | |
| 650 | 4 | |a 615 | |
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| 700 | 0 | |a YU HONG |4 aut | |
| 700 | 0 | |a SU ZUQIANG |4 aut | |
| 700 | 0 | |a LIU YUNSHENG |4 aut | |
| 700 | 0 | |a DAI JIN |4 aut | |
| 700 | 0 | |a LIU JINGFENG |4 aut | |
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