http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114121285-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_91c775ef7af1320ca25f51ff4bec84f9 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06Q10-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-241 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-214 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-30 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-24323 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-30 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06Q10-04 |
| filingDate | 2021-12-02-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_1abf7782da90230fc2bb0fd00d9e2e46 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_df3816ce878ed0400cfc82deee731973 |
| publicationDate | 2022-03-01-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-114121285-A |
| titleOfInvention | Kidney injury risk data prediction system, prediction method, computer equipment and medium |
| abstract | The invention belongs to the technical field of model construction, and discloses a kidney injury risk data prediction system, a prediction method, computer equipment and a medium. The kidney injury risk data prediction system includes: a data screening module, a data extraction and classification module, a variable extraction module, Variable Screening Module, Model Building Module, and Validation Module. The present invention innovatively provides a machine learning prediction model for evaluating the risk of nephrotoxicity caused by TDF in PLWH, which can effectively identify the situation that PLWH has a risk of renal injury in the treatment of TDF. The variables in the model of the present invention are easy to reproduce, can be applied in clinical practice, and highlight the prospect of prospective machine learning. The predictor variables used in the model of the present invention are easily assessed during clinical follow-up, allowing easy identification of at-risk data and guiding prognosis. |
| priorityDate | 2021-12-02-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 105.