| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_18b5dcd6ce09f89a3223072eedb611f7 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B2562-0219
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B2503-08 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-6898
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-1118
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-1124
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H40-67
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/A61B5-7275
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-4803
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-7475
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-747
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-7435
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-50
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H10-60
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-1123
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-165
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-162 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-16
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-11
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-50 |
| filingDate |
2019-02-01-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_67e5a12d8077cca82bed052b667cf4c7 |
| publicationDate |
2020-08-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-2020245949-A1 |
| titleOfInvention |
Forecasting Mood Changes from Digital Biomarkers |
| abstract |
The present invention extends to methods, systems, and computer program products for forecasting mood changes from digital biomarkers and more generally for forecasting changes in a neuropsychological clinical assessment. User interaction data indicative of a user's interaction with a mobile device is passively captured over a period of time. A function mapping (modeling a neuropsychological test for a brain health metric) is executed to compute a digital biomarker for a brain health metric from the captured user interaction data. A prior digital biomarker for the brain health metric (computed from previously captured user interaction data by executing the function mapping) is accessed. A difference is detected between the digital biomarker and the prior digital biomarker. A change in a score of the neuropsychological test score is forecast to occur within a specified time range in the future based on the detected differences. The forecast change is output. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2022351855-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-115658933-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11605464-B2 |
| priorityDate |
2019-02-01-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |