| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_69ece09bafee7e0c49d1f7951a3faa2c |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16C20-70 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16C20-70
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16C20-50
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16C20-30 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16C20-50
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16C20-70 |
| filingDate |
2022-09-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_bb8df59ba3bea2dfc22bc8b07eca13a7 |
| publicationDate |
2023-01-19-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-2023018216-A1 |
| titleOfInvention |
Population pk/pd linking parameter analysis using deep learning |
| abstract |
A method and system for predicting a set of linking parameters that relate pharmacokinetic and pharmacodynamic effects. One or more processors receive a population dataset that comprises a population pharmacokinetic (PK) dataset and a population pharmacodynamic (PD) dataset. The one or more processors transform the population dataset into a plurality of data density images that includes a PK data density image and a PD data density image. The one or more processors predict the set of linking parameters using the plurality of data density images. |
| priorityDate |
2020-03-25-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |