http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2020109381-A1
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_75b86e50a7529f6158517db14a0b81df |
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-00 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H20-30 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G08B31-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H40-63 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G08B31-00 |
| filingDate | 2019-11-27-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_57f73bbb66c1debf49c63393ae39105a |
| publicationDate | 2020-06-04-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | WO-2020109381-A1 |
| titleOfInvention | Predicting critical alarms |
| abstract | Embodiments propose methods and system for predicting the occurrence of critical alarms in response to the occurrence of less severe, non-critical alarms. It is proposed to use a machine-learning model trained to discern whether a non-critical alarm will be followed by a critical alarm within a particular time period, e.g. whether the non-critical alarm will develop into a critical alarm. Unlike existing alarm systems which are merely threshold based, this approach uses physiological data from a window of data. This window of data can be expected to carry more information than a simple breach of the threshold. |
| priorityDate | 2018-11-27-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 18.