PMC:7160614 / 26003-26702 JSONTXT

{"project":"LitCovid-PubTator","denotations":[{"id":"326","span":{"begin":130,"end":138},"obj":"Species"},{"id":"327","span":{"begin":120,"end":129},"obj":"Disease"},{"id":"328","span":{"begin":156,"end":164},"obj":"Disease"},{"id":"329","span":{"begin":336,"end":344},"obj":"Disease"},{"id":"330","span":{"begin":680,"end":688},"obj":"Disease"},{"id":"331","span":{"begin":689,"end":698},"obj":"Disease"}],"attributes":[{"id":"A326","pred":"tao:has_database_id","subj":"326","obj":"Tax:9606"},{"id":"A327","pred":"tao:has_database_id","subj":"327","obj":"MESH:D011014"},{"id":"A328","pred":"tao:has_database_id","subj":"328","obj":"MESH:C000657245"},{"id":"A329","pred":"tao:has_database_id","subj":"329","obj":"MESH:C000657245"},{"id":"A330","pred":"tao:has_database_id","subj":"330","obj":"MESH:C000657245"},{"id":"A331","pred":"tao:has_database_id","subj":"331","obj":"MESH:D007239"}],"namespaces":[{"prefix":"Tax","uri":"https://www.ncbi.nlm.nih.gov/taxonomy/"},{"prefix":"MESH","uri":"https://id.nlm.nih.gov/mesh/"},{"prefix":"Gene","uri":"https://www.ncbi.nlm.nih.gov/gene/"},{"prefix":"CVCL","uri":"https://web.expasy.org/cellosaurus/CVCL_"}],"text":"In this multi-center study, statistical analysis was performed in comparing imaging and clinical manifestations between pneumonia patients with and without COVID-19. Eighteen radiological semantic features and seventeen clinical features were identified to be significantly different between the two groups (p \u003c 0.05). Three models for COVID-19 diagnosis were developed based on the refined features. The models were validated in the both primary and validation cohorts and achieved an AUC as high as 0.986. These models will play an essential role for early and easy-to-access diagnosis, especially when there are not enough RT-PCT kits or experimental platforms to test for the COVID-19 infection."}

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T98","span":{"begin":120,"end":129},"obj":"Disease"},{"id":"T99","span":{"begin":156,"end":164},"obj":"Disease"},{"id":"T100","span":{"begin":336,"end":344},"obj":"Disease"},{"id":"T101","span":{"begin":629,"end":632},"obj":"Disease"},{"id":"T103","span":{"begin":680,"end":688},"obj":"Disease"},{"id":"T104","span":{"begin":689,"end":698},"obj":"Disease"}],"attributes":[{"id":"A98","pred":"mondo_id","subj":"T98","obj":"http://purl.obolibrary.org/obo/MONDO_0005249"},{"id":"A99","pred":"mondo_id","subj":"T99","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A100","pred":"mondo_id","subj":"T100","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A101","pred":"mondo_id","subj":"T101","obj":"http://purl.obolibrary.org/obo/MONDO_0008296"},{"id":"A102","pred":"mondo_id","subj":"T101","obj":"http://purl.obolibrary.org/obo/MONDO_0015104"},{"id":"A103","pred":"mondo_id","subj":"T103","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A104","pred":"mondo_id","subj":"T104","obj":"http://purl.obolibrary.org/obo/MONDO_0005550"}],"text":"In this multi-center study, statistical analysis was performed in comparing imaging and clinical manifestations between pneumonia patients with and without COVID-19. Eighteen radiological semantic features and seventeen clinical features were identified to be significantly different between the two groups (p \u003c 0.05). Three models for COVID-19 diagnosis were developed based on the refined features. The models were validated in the both primary and validation cohorts and achieved an AUC as high as 0.986. These models will play an essential role for early and easy-to-access diagnosis, especially when there are not enough RT-PCT kits or experimental platforms to test for the COVID-19 infection."}

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T160","span":{"begin":667,"end":671},"obj":"http://purl.obolibrary.org/obo/UBERON_0000473"}],"text":"In this multi-center study, statistical analysis was performed in comparing imaging and clinical manifestations between pneumonia patients with and without COVID-19. Eighteen radiological semantic features and seventeen clinical features were identified to be significantly different between the two groups (p \u003c 0.05). Three models for COVID-19 diagnosis were developed based on the refined features. The models were validated in the both primary and validation cohorts and achieved an AUC as high as 0.986. These models will play an essential role for early and easy-to-access diagnosis, especially when there are not enough RT-PCT kits or experimental platforms to test for the COVID-19 infection."}

{"project":"LitCovid-PD-HP","denotations":[{"id":"T35","span":{"begin":120,"end":129},"obj":"Phenotype"}],"attributes":[{"id":"A35","pred":"hp_id","subj":"T35","obj":"http://purl.obolibrary.org/obo/HP_0002090"}],"text":"In this multi-center study, statistical analysis was performed in comparing imaging and clinical manifestations between pneumonia patients with and without COVID-19. Eighteen radiological semantic features and seventeen clinical features were identified to be significantly different between the two groups (p \u003c 0.05). Three models for COVID-19 diagnosis were developed based on the refined features. The models were validated in the both primary and validation cohorts and achieved an AUC as high as 0.986. These models will play an essential role for early and easy-to-access diagnosis, especially when there are not enough RT-PCT kits or experimental platforms to test for the COVID-19 infection."}

{"project":"LitCovid-sentences","denotations":[{"id":"T288","span":{"begin":0,"end":165},"obj":"Sentence"},{"id":"T289","span":{"begin":166,"end":318},"obj":"Sentence"},{"id":"T290","span":{"begin":319,"end":400},"obj":"Sentence"},{"id":"T291","span":{"begin":401,"end":507},"obj":"Sentence"},{"id":"T292","span":{"begin":508,"end":699},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"In this multi-center study, statistical analysis was performed in comparing imaging and clinical manifestations between pneumonia patients with and without COVID-19. Eighteen radiological semantic features and seventeen clinical features were identified to be significantly different between the two groups (p \u003c 0.05). Three models for COVID-19 diagnosis were developed based on the refined features. The models were validated in the both primary and validation cohorts and achieved an AUC as high as 0.986. These models will play an essential role for early and easy-to-access diagnosis, especially when there are not enough RT-PCT kits or experimental platforms to test for the COVID-19 infection."}