{"id":"W3204720196","doi":"10.1109/ijcnn52387.2021.9534188","title":"Predictive Analytics of COVID-19 with Neural Networks","year":2021,"lang":"en","type":"article","venue":"","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Autoencoder; Computer science; Predictive analytics; Coronavirus disease 2019 (COVID-19); Analytics; Artificial neural network; Artificial intelligence; Machine learning; Data analysis; Identification (biology); Test data; Predictive modelling; Data mining; Disease; Medicine; Infectious disease (medical specialty); Pathology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001181694,0.0001162529,0.0003059818,0.00007526211,0.00003689455,0.00001110102,0.00005833056,0.00007165135,0.0004888439],"category_scores_gemma":[0.0005637065,0.00008880752,0.00007496922,0.0005411821,0.0001173018,0.00004109045,0.00005599385,0.000165062,0.000002722485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001456301,"about_ca_system_score_gemma":0.0006945599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001761151,"about_ca_topic_score_gemma":0.0001684218,"domain_scores_codex":[0.9990119,0.00004444114,0.0002071089,0.0002652992,0.0002795564,0.0001916611],"domain_scores_gemma":[0.9985822,0.0004268611,0.00007350879,0.0003864287,0.0002558862,0.0002750916],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001389661,0.001319119,0.4259007,0.0007608004,0.0009449581,0.002098677,0.001179693,0.404116,0.0007474158,0.001396874,0.1575309,0.002615165],"study_design_scores_gemma":[0.006115612,0.001735183,0.05460168,0.0002668727,0.001247152,0.0003898023,0.001138848,0.8792863,0.004981613,0.0001133912,0.04974524,0.0003783232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3383616,0.001521838,0.371178,0.2781626,0.0005522317,0.001271853,0.00005043205,0.0006838173,0.008217569],"genre_scores_gemma":[0.9484431,0.0000453743,0.001391,0.0489241,0.0001200182,0.000007650493,0.00003074302,0.00001995545,0.001018024],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6100816,"threshold_uncertainty_score":0.5352498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0334134027927777,"score_gpt":0.3260311216895456,"score_spread":0.2926177188967679,"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."}}