MORBIDITY DETERMINATION ASSISTANCE DEVICE, MORBIDITY DETERMINATION ASSISTANCE METHOD, AND MORBIDITY DETERMINATION ASSISTANCE PROGRAM
The present invention improves cancer detection accuracy. A disease determination assistance device according to the present invention comprises: a preprocessing unit which rearranges, into a prescribed order, analysis results obtained by analyzing a plurality of biomarkers in a biological sample derived from a subject and which converts the rearranged analysis results into images; and a determination assistance unit which outputs information, representing the possibility that the subject has a disease, by using a learned model which learned, through deep learning, the relationship between a disease morbidity state and information converted into an image after rearranging the image and the analysis results of a plurality of biomarkers in biological samples derived from subjects having a specific disease and subjects not having the disease..
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Patent| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Europäisches Patentamt - (2020) vom: 17. Dez. Zur Gesamtaufnahme - year:2020 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
MIKAMI MIKIO [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Anmerkungen: |
Source: www.epo.org (no modifications made), First posted: 2020-12-17, Last update posted on www.tib.eu: 2022-07-02, Last updated: 2023-02-09 |
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| Patentnummer: |
WO2020250995 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
EPA010365818 |
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| 520 | |a The present invention improves cancer detection accuracy. A disease determination assistance device according to the present invention comprises: a preprocessing unit which rearranges, into a prescribed order, analysis results obtained by analyzing a plurality of biomarkers in a biological sample derived from a subject and which converts the rearranged analysis results into images; and a determination assistance unit which outputs information, representing the possibility that the subject has a disease, by using a learned model which learned, through deep learning, the relationship between a disease morbidity state and information converted into an image after rearranging the image and the analysis results of a plurality of biomarkers in biological samples derived from subjects having a specific disease and subjects not having the disease. | ||
| 650 | 4 | |a G16B: Bioinformatics, i.e. information and communication technology [ict] specially adapted for genetic or protein-related data processing in computational molecular biology | |
| 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 G01N: Investigating or analysing materials by determining their chemical or physical properties (measuring or testing processes other than immunoassay, involving enzymes or microorganisms c12m, c12q) | |
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