http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114974585-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_e02b4ed18c32902c3870020626369c7d |
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/Y02A90-10 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-23 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-214 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-00 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/G06F18-2415 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-30 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N20-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62 |
| filingDate | 2022-05-27^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_b507b7ffd4c5362845cece0eab041cd7 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_651f1ed86a75eba5dc4d07ab65f8704a http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_63df633f2ed31cec5e843e8171c603e5 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_2bc6bdda79d52e3d6ad6df47f0813de6 |
| publicationDate | 2022-08-30^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-114974585-A |
| titleOfInvention | A method for constructing an early risk prediction and assessment model for metabolic syndrome in pregnancy |
| abstract | The invention discloses a method for constructing an early risk prediction and evaluation model for metabolic syndrome during pregnancy, comprising the steps of: (1) acquiring multi-source heterogeneous data, and preprocessing it to obtain metabolic-related data; (2) screening and Adverse pregnancy outcomes that are highly correlated with Gms; (3) use extreme gradient boosting (XGBoost) combined with the Stacking framework to build a prediction model, and input the prediction model according to the adverse pregnancy outcomes determined in (2) as prediction labels; (4) Calculate predictions based on Shapley values The feature importance of each modeling factor in the model; (5) According to the feature importance of the modeling factor in (4), a risk stratification model is established based on the clustering algorithm, and the Gms risk level is obtained. Through the present invention, early prediction of clinical GMS can be realized, GMS-related prediction indicators can be found as soon as possible, risks can be avoided to the neonatal period, and the risk of long-term metabolic diseases of offspring can be reduced, which is important for preventing and reducing the occurrence of GMS. significance. |
| isCitedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-116013520-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-116307742-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-116307742-B |
| priorityDate | 2022-05-27^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
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