http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2022122996-A1
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_b01dcd61775e996fac631d4b10c4e87f http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_43a72884e25aaecffebe0942132d704f http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_37f4922dfb7777b019e504b885211b8e http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_986e524a617baf477896db79fc7232da http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_e3b4f2ef8f062dcde3a2b7cd81748472 |
| classificationCPCInventive | 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/G16H50-70 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-4094 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-7267 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-30 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-00 |
| filingDate | 2021-12-10-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_1704ce48a31cf5125ed6c0d88b8b3858 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_61e16f4826050bc90f5aed36a41356fc http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_ef9fe5a6457dde808761c680534af61d |
| publicationDate | 2022-06-16-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | WO-2022122996-A1 |
| titleOfInvention | A computer-implemented model for predicting occurrence of a seizure and training method thereof |
| abstract | The invention relates to a method for training a model for predicting occurrence of an epileptic seizure, the method comprising performing a supervised training over a training dataset of a nonlinear binary classification model configured to receive as input the evaluation, by a patient, of the intensity of each prodromal symptom among a predefined set of prodromal symptoms, and to output a classification of said patient belonging either to a pre-ictal or inter-ictal state, and the training dataset comprises data inputs obtained from a plurality of epileptic patients, each data input comprising an evaluation, by a patient, of the intensity of each of the predefined set of prodromal symptoms, each data input being further associated to an indication of said patient belonging to a pre-ictal or inter-ictal state at the time of the evaluation. The invention also relates to a prediction model obtained accordingly, and a computing device for implementing said prediction model. |
| priorityDate | 2020-12-11-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 25.