{"id":"W3155019106","doi":"10.1038/s41598-021-95537-y","title":"A bagging dynamic deep learning network for diagnosing COVID-19","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Key Research and Development Program of China; South China University of Technology; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Coronavirus disease 2019 (COVID-19); Computer science; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Artificial intelligence; Deep learning; Machine learning; Virology; Medicine; Internal medicine; Infectious disease (medical specialty); Outbreak","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006247548,0.0009764183,0.000701867,0.0009557446,0.0004239171,0.0007168733,0.001109262,0.001141649,0.001059544],"category_scores_gemma":[0.001560784,0.0003060706,0.0005579758,0.0005432667,0.0003001369,0.001151932,0.0008369189,0.0009985736,0.0005195799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007898942,"about_ca_system_score_gemma":0.0009006659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00580338,"about_ca_topic_score_gemma":0.006177645,"domain_scores_codex":[0.9996093,0.0000602925,0.00002919367,0.0001339992,0.00009649704,0.00007068564],"domain_scores_gemma":[0.9996576,0.0001114013,0.00003731907,0.00003034766,0.0001282422,0.00003508672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005527087,0.000548362,0.01746316,0.0001428481,0.0001666897,0.0004256274,0.000112354,0.126286,0.016004,0.00177928,0.01114492,0.8253739],"study_design_scores_gemma":[0.00001698278,0.0001221647,0.001634533,0.00002311249,0.0000470991,0.0001769299,0.00002941876,0.9898798,0.005143023,0.001606274,0.001299272,0.00002136199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1939703,0.004149027,0.7880255,0.001661685,0.0005896223,0.0001865666,0.0007480777,0.004805482,0.005863811],"genre_scores_gemma":[0.8879514,0.001094732,0.102297,0.001097419,0.0001640404,0.0001434409,0.001422212,0.00006560956,0.005764126],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00580338,"threshold_uncertainty_score":0.01153922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02768039842801246,"score_gpt":0.3390551846205234,"score_spread":0.3113747861925109,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}