| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_7640a1f23aa57655340c1d463d83a076
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_af9b4353f11ba6702888ff8d5fe57b36
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_2884848a1df2a96fcc19c6228404db46
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_729aafb56e60eafac003dd9af88834c1 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-201
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H10-60
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-7267
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-4848
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-7275
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-50
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-20 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-00 |
| filingDate |
2012-10-27-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_2cc2671f78e43910a4b45425e11b776d
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_19b1505e9b3be83e465fb3f15e7e033e
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_ff9be5c6f6f501359993a5d23ff19850
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_64f3b781b70bd66c1add93ad67963fd7 |
| publicationDate |
2016-07-21-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-2016206249-A9 |
| titleOfInvention |
Bayesian modeling of pre-transplant variables accurately predicts kidney graft survival |
| abstract |
An embodiment of the invention provides a method for determining a patient-specific probability of renal transplant survival. The method collects clinical parameters from a plurality of renal transplant donor and patient to create a training database. A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated. Clinical parameters are collected from an individual patient/donor; and, such clinical parameters are input into the fully unsupervised Bayesian Belief Network model via a graphical user interface. The patient-specific probability of disease is output from the fully unsupervised Bayesian Belief Network model and sent to the graphical user interface for use by a clinician in pre-operative organ matching. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of transplant survival. |
| priorityDate |
2008-10-15-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |