| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_3eab1ad612763f4308254f36d2e13b8f
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_180c0f6214ab9965f6f984b42bd5565f |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/Y10S128-925
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-341 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-7267
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16Z99-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-346
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-318 |
| classificationIPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-04 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-0452
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-0402
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F17-10
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B10-00 |
| filingDate |
2001-01-26-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_dd4786f00a4968ecfe0f836536f3fbde
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_294e637b21b97cfdad20d363ce93a401 |
| publicationDate |
2001-08-16-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
WO-0158350-A1 |
| titleOfInvention |
Provision of decision support for acute myocardial infarction |
| abstract |
The present invention provides methods and apparatuses, which make use of at least one trained and tuned artificial neural network (16) to generate decision regions (32) in the n-dimensional space of n input variables associated with AMI. The set of measured variables (30) is related to the decision regions (32), in order to provide decision support. Preferably, the decision regions (32) are graphically visualized as areas in a two-dimensional diagram. Preferably, the artificial neural network (16) is trained by patient specific parameters. The variables associated with AMI (30) are preferably selected as biochemical markers and/or quantities derived from continuous/intermittent ECG/VCG. The performance of the artificial neural network (16) is preferably optimally tuned to clinical requirements on predictive values of the artificial neural network output in given prevalence situations. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/EP-1519303-A2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/EP-1519303-A3
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11568991-B1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2009033831-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-107887010-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/EP-1320061-A3
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/EP-1320061-A2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/JP-2007505726-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-7977105-B2 |
| priorityDate |
2000-02-10-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |