PMC:7782580 / 1338-1820
Annnotations
LitCovid-PubTator
Id | Subject | Object | Predicate | Lexical cue | tao:has_database_id |
---|---|---|---|---|---|
19 | 22-50 | Disease | denotes | upper respiratory infections | MESH:D012141 |
20 | 200-208 | Disease | denotes | COVID-19 | MESH:C000657245 |
21 | 209-218 | Disease | denotes | infection | MESH:D007239 |
49 | 255-279 | Disease | denotes | Coronavirus disease 2019 | MESH:C000657245 |
50 | 281-289 | Disease | denotes | COVID-19 | MESH:C000657245 |
51 | 301-319 | Disease | denotes | infectious disease | MESH:D003141 |
LitCovid-PD-HP
Id | Subject | Object | Predicate | Lexical cue | hp_id |
---|---|---|---|---|---|
T1 | 28-50 | Phenotype | denotes | respiratory infections | http://purl.obolibrary.org/obo/HP_0011947 |
LitCovid-sentences
Id | Subject | Object | Predicate | Lexical cue |
---|---|---|---|---|
T11 | 52-240 | Sentence | denotes | Compared to expert evaluation of the images, the neural network achieved upwards of 99% specificity, showing promise for the automated detection of COVID-19 infection in clinical settings. |
T12 | 242-254 | Sentence | denotes | Introduction |