| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_75b86e50a7529f6158517db14a0b81df |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-70
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-6262
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-048
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-0481
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-7267
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-7275
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-217
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-6261
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-201
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2163 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N20-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 |
| filingDate |
2020-04-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate |
2022-10-18-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_85fe4656f715fbc36d6998b2abcd77f0
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_7077edea5ac17b3d6feb8d62999d5105
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_218999b5cb3275feed9f4a503a1f674f
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_5d0fccb81739d42dc5f66760398dee3f |
| publicationDate |
2022-10-18-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-11475302-B2 |
| titleOfInvention |
Multilayer perceptron based network to identify baseline illness risk |
| abstract |
A method for training a baseline risk model, including: pre-processing input data by normalizing continuous variable inputs and producing one-hot input features for categorical variables; providing definitions for clean input data and dirty input data based upon various input data related to a patient condition; segmenting the input data into clean input data and dirty input data, wherein the clean input data includes a first subset and a second subset, where the first subset and the second subset include all of the clean input data and are disjoint; training a machine learning model using the first subset of the clean data; and evaluating the performance of the trained machine learning model using the second subset of the clean input data and the dirty input data. |
| priorityDate |
2019-04-05-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |