| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_69108fc12b940ede105638f37f06080b |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-063 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-044
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-681
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-082
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-0205
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-02055
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-14532
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-0454
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-7264
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-7271 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-0205
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-00 |
| filingDate |
2020-06-16-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_2952465046fb2a8b9932765a89662e4c
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_72959405f8595731b7990ca0015d3afb
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_a8242fd6bf5c5be4a522cd4e13f18a63
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_0cfb982338112b09f8208d2491590648 |
| publicationDate |
2022-08-04-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-2022240864-A1 |
| titleOfInvention |
System and method for wearable medical sensor and neural network based diabetes analysis |
| abstract |
According to various embodiments, a machine-learning based system for diabetes analysis is disclosed. The system includes one or more processors configured to interact with a plurality of wearable medical sensors (WMSs). The processors are configured to receive physiological data from the WMSs and demographic data from a user interface. The processors are further configured to train at least one neural network based on a grow-and-prune paradigm to generate at least one diabetes inference model. The neural network grows at least one of connections and neurons based on gradient information and prunes away at least one of connections and neurons based on magnitude information. The processors are also configured to output a diabetes-based decision by inputting the received physiological data and demographic data into the generated diabetes inference model. |
| priorityDate |
2019-06-17-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |