http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2022203140-A1
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_465b607edb4c88aeefb4958e154efaec |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-50 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-70 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-145 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-20 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-50 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-70 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N20-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-145 |
| filingDate | 2021-09-27-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_07b7497947d9aa6ea93f22f3b448a81e http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_3b34e4eb7bc8a8ba037a0ec010f686d6 |
| publicationDate | 2022-09-29-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | WO-2022203140-A1 |
| titleOfInvention | Clinical decision support system and method for acute kidney injury using recurrent neural network prediction model |
| abstract | A clinical decision support system using an RNN machine learning model according to one embodiment of the present invention comprises: an input unit for sequentially receiving inputs of patient-related information; a first prediction model which uses an RNN machine learning algorithm and predicts the probability of acute kidney injury (AKI) occurring in a patient on the basis of the inputted patient-related information; a second prediction model which uses the RNN machine learning algorithm and predicts changes in a patient's serum creatinine level on the basis of the prediction result of the first prediction model; and a plot generation unit which generates one or more plots visually representing the effect that each of one or more feature variables included in the patient-related information have on the prediction result. According to the embodiment, the probability of AKI occurrence and the changes in serum creatinine level in a hospitalized patient can be predicted in real time by using the RNN machine learning model, and plots which visually represent relationships between the result and the feature variables which affect the result of the prediction model can be provided to provide insight into clinical decisions. |
| priorityDate | 2021-03-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 66.