http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111863281-B
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H70-40 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H70-40 |
| filingDate | 2020-07-29-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2021-08-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2021-08-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-111863281-B |
| titleOfInvention | A personalized drug adverse reaction prediction system, equipment and medium |
| abstract | The invention provides a method, system, equipment and medium for predicting the adverse reaction of personalized medicine, which belong to the technical field of biomedicine. The present invention proposes a multi-task learning model (KEMULA) based on multi-kernel function learning to replace the traditional "one size fits all" and "completely individualized" learning methods. More specifically, the model computes and ranks each patient's risk of developing ADR by learning a constrained personalized ADR ranking function assuming the model's shared function. This function is called the Personalized ADR Ranking Function, which is a linear combination of several scoring functions that calculate a patient's risk of developing an associated ADR. The model also incorporates Laplacian regularization to ensure that the variable information trained by the personADRank function for similar patients is close, which improves the model's causal relationship (true positive) for the association between a given patient and the corresponding ADR, so The present invention has good practical application value. |
| priorityDate | 2020-07-29-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 22.