PMC:7782580 / 945-1399 JSONTXT

{"project":"LitCovid-PubTator","denotations":[{"id":"11","span":{"begin":188,"end":196},"obj":"Disease"},{"id":"17","span":{"begin":321,"end":329},"obj":"Disease"},{"id":"18","span":{"begin":330,"end":339},"obj":"Disease"},{"id":"19","span":{"begin":415,"end":443},"obj":"Disease"}],"attributes":[{"id":"A11","pred":"tao:has_database_id","subj":"11","obj":"MESH:C000657245"},{"id":"A17","pred":"tao:has_database_id","subj":"17","obj":"MESH:C000657245"},{"id":"A18","pred":"tao:has_database_id","subj":"18","obj":"MESH:D007239"},{"id":"A19","pred":"tao:has_database_id","subj":"19","obj":"MESH:D012141"}],"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":"reas in the images and five clinical indicators, resulting in high accuracy of the classification framework. The proposed method represents a potential computer-aided diagnosis method for COVID-19 in clinical practice.\n\nLiang, Gu and other colleagues develop a convoluted neural network (CNN)-based framework to diagnose COVID-19 infection from chest X-ray and computed tomography images, and comparison with other upper respiratory infections. Compared "}

{"project":"LitCovid-PD-HP","denotations":[{"id":"T1","span":{"begin":421,"end":443},"obj":"Phenotype"}],"attributes":[{"id":"A1","pred":"hp_id","subj":"T1","obj":"http://purl.obolibrary.org/obo/HP_0011947"}],"text":"reas in the images and five clinical indicators, resulting in high accuracy of the classification framework. The proposed method represents a potential computer-aided diagnosis method for COVID-19 in clinical practice.\n\nLiang, Gu and other colleagues develop a convoluted neural network (CNN)-based framework to diagnose COVID-19 infection from chest X-ray and computed tomography images, and comparison with other upper respiratory infections. Compared "}

{"project":"LitCovid-sentences","denotations":[{"id":"T9","span":{"begin":109,"end":218},"obj":"Sentence"},{"id":"T10","span":{"begin":220,"end":444},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"reas in the images and five clinical indicators, resulting in high accuracy of the classification framework. The proposed method represents a potential computer-aided diagnosis method for COVID-19 in clinical practice.\n\nLiang, Gu and other colleagues develop a convoluted neural network (CNN)-based framework to diagnose COVID-19 infection from chest X-ray and computed tomography images, and comparison with other upper respiratory infections. Compared "}