{"id":"W4312125493","doi":"10.3390/v14122761","title":"Clinical Application of Detecting COVID-19 Risks: A Natural Language Processing Approach","year":2022,"lang":"en","type":"article","venue":"Viruses","topic":"Topic Modeling","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto; Toronto Metropolitan University","funders":"","keywords":"Novelty; Computer science; Machine learning; Artificial intelligence; Artificial neural network; Pipeline (software); Pandemic; Transformer; F1 score; Task (project management); Named-entity recognition; Coronavirus disease 2019 (COVID-19); Natural language processing; Infectious disease (medical specialty); Disease; Medicine; Psychology; Engineering","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":[0.002378423,0.0009320679,0.0006509942,0.004940104,0.0004901198,0.001360292,0.0008974886,0.001310823,0.001543251],"category_scores_gemma":[0.006996361,0.0002661856,0.001235384,0.002049116,0.0004536812,0.001467513,0.00105792,0.001274589,0.001079453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007037015,"about_ca_system_score_gemma":0.001611838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005134791,"about_ca_topic_score_gemma":0.00658565,"domain_scores_codex":[0.9978901,0.0006839324,0.0003441389,0.0006141209,0.0003370976,0.000130657],"domain_scores_gemma":[0.9948638,0.00328699,0.0006514195,0.0003136982,0.000710638,0.0001734479],"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":[0.000892185,0.0009387597,0.1184541,0.00192599,0.0004173647,0.003769913,0.001381989,0.02316982,0.0424036,0.01045209,0.03730719,0.7588869],"study_design_scores_gemma":[0.0001320628,0.0005424026,0.07245379,0.00040945,0.0004632412,0.006421732,0.001830949,0.7953373,0.02522623,0.04721457,0.04976171,0.0002066419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1642945,0.006557185,0.7823772,0.009431002,0.0006945726,0.001632024,0.02177487,0.005130467,0.008108162],"genre_scores_gemma":[0.5864234,0.002010196,0.3894946,0.001329079,0.0007860183,0.0006505582,0.01649764,0.0001243181,0.002684281],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005134791,"threshold_uncertainty_score":0.01257843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1179992466228597,"score_gpt":0.404890190535899,"score_spread":0.2868909439130393,"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."}}