| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_f555c332d3fdb1aa158d2f5190a7ad97 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H10-60
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F2216-03
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N7-01
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B8-488 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-7275
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B8-5215
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H10-60
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B8-0883
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-50
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-70
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-7267
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B8-488
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-088 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N20-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B8-12 |
| filingDate |
2019-02-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_0bbddb4827a89a75bbc5050472cc74b9
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_78a3d3dc05e706799a57925b9c607a85 |
| publicationDate |
2019-08-15-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
WO-2019153039-A1 |
| titleOfInvention |
Systems and methods for ai-assisted echocardiography |
| abstract |
A method for processing a sparsely populated data source comprising: retrieving data from a sparsely populated data source whose records comprise at least one unpopulated data field corresponding to a medical measurement; dividing the data into a training data set and a validation data set; analysing the training data set using a non-linear function approximation algorithm to obtain a trained model and measurement prediction protocols for populating unpopulated fields in the training data set; using the measurement prediction protocols to predict data for the unpopulated data fields; analysing the training dataset on the basis of predefined disease conditions in known patient records to form a disease model which predicts a probability of a disease condition; validating the disease model by analysing the validation dataset and determining a validation error; repeating steps to minimise the validation error and predict a probable disease state for each patient record. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2022266651-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2021122345-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11704803-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2020257592-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111739617-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2021097460-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11709910-B1 |
| priorityDate |
2018-02-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |