| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_e757fd4fedc4fe825bb81b1b466a0947 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N2800-2821 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B40-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N33-6896
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N5-01
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2415
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H70-60
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-70
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-6277
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-20 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B40-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N20-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01N33-68
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-70 |
| filingDate |
2019-09-24-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_fcce0f736ad952a83f5c1e538947120f
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_794cba8b40b3ff39f00f208c0f41cc81
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_6062ea2096ced56d44e37e4e9f2a004c
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_5d609c027ba1386f611ace2909f7f96d
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_ee2b86116514f1d3a7d4af6761bee321
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_8a04886f29c8b0eff5cad18c6a043fd4 |
| publicationDate |
2021-03-25-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-2021090746-A1 |
| titleOfInvention |
Machine learning for amyloid and tau pathology prediction |
| abstract |
Method and apparatus for predicting amyloid beta (Aβ) and phosphorylated tau (p-tau) biomarker levels in the cerebrospinal fluid (CSF) of patients. Embodiments include determining current values for a plurality of easily-measurable attributes of a first patient. Embodiments include analyzing data associated with a cohort of patients having known measurements of Aβ and p-tau biomarker levels, including determined values for the plurality of easily-measureable attributes. Embodiments include generating a predicted value for Aβ and/or p-tau biomarker levels for the first patient. Embodiments include generating a risk of the first patient developing AD at a future time, generating a probability of a patient's predicted rate of decline, and/or generating a probability of a patient's age at the onset of dementia, based on the predicted values for Aβ and/or p-tau biomarker levels. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2022028503-A1 |
| priorityDate |
2019-09-24-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |