| Predicate |
Object |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10081
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30061
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20081 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H30-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2132
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-11
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0012
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-55
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-241
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-20 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-764
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-77
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-11
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H30-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-55
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-82 |
| filingDate |
2021-02-03-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate |
2022-09-02-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate |
2022-09-02-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
CN-113034434-B |
| titleOfInvention |
A multi-factor artificial intelligence analysis method to predict the severity of COVID-19 |
| abstract |
The invention discloses a multi-factor artificial intelligence survival model analysis method, which includes step 1: performing computer-aided processing and feature extraction on chest CT images of COVID-19 patients; step 2: performing data information on COVID-19 patients from time to Data analysis of events and modeling of survival prognosis. Advantages of the present invention are: Advantages of the present invention include that covariates can also be included, and a predictive model combining CT imaging features and baseline information is significantly improved in predicting severe attacks. |
| priorityDate |
2021-02-03-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |