http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2019055945-A1
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_2472734ecfa7ba7426563077bdab00fb |
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N7-01 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-10 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H10-60 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B40-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B20-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08 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/G06N7-01 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H20-40 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-70 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B40-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B25-00 |
| filingDate | 2018-09-17-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_42399d884015e6637a05bf1950641e57 |
| publicationDate | 2019-03-21-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | WO-2019055945-A1 |
| titleOfInvention | SYSTEM FOR RECOMMENDING A SERIES OF TREATMENTS |
| abstract | A system for generating a series of treatment recommendation ("TOC") device for recommending TOCs for patients using machine learning. A recommendation system for a series of automatic learning processes ("MLTR") results in a TOC recommendation device using training data that includes a feature vector and a marker for each patient in a group of patients. The features of the feature vector may include features derived from the patient's data. A marker is a series of treatments for a patient designated as a series of marking treatments. The MLTR system generates the learning data from the patient data collected over time. The MLTR system then uses the training data to drive the TOC recommendation device using an automatic learning technique. Once the TOC recommendation device has been trained, the TOC recommendation device may be applied to a patient's patient data feature vector to generate a MLTR-recommended treatment series for the patient. |
| priorityDate | 2017-09-18-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 48.