{"id":"W4280601278","doi":"10.18280/ria.360216","title":"Differentiating Between COVID-19 and Tuberculosis Using Machine Learning and Natural Language Processing","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Durham College","funders":"","keywords":"Tuberculosis; Medicine; Pandemic; Coronavirus disease 2019 (COVID-19); Disease; Receiver operating characteristic; Natural history; Pediatrics; Artificial intelligence; Family medicine; Internal medicine; Infectious disease (medical specialty); Pathology; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.002875726,0.000563972,0.0005089573,0.005210428,0.0004814186,0.001887641,0.0005010683,0.000961205,0.001155355],"category_scores_gemma":[0.01100781,0.00022034,0.000964199,0.001761844,0.0005102426,0.001539996,0.0008828082,0.0008933375,0.0006808719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005130769,"about_ca_system_score_gemma":0.0007961721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0023998,"about_ca_topic_score_gemma":0.003440971,"domain_scores_codex":[0.9971619,0.001016841,0.0006655328,0.0004565191,0.0004585203,0.0002407696],"domain_scores_gemma":[0.9912116,0.006533931,0.001162478,0.000244367,0.000613027,0.000234538],"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":[0.000709602,0.0009189239,0.6611933,0.0007227645,0.0002215403,0.005142519,0.001254449,0.01201766,0.01094775,0.001135175,0.005610556,0.3001259],"study_design_scores_gemma":[0.0001027671,0.001011342,0.5801817,0.0009296185,0.0003489991,0.01127266,0.007670762,0.3584578,0.01555861,0.01098513,0.01325482,0.0002257556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9463253,0.00190644,0.04389956,0.001335252,0.0001521619,0.0003280431,0.0027558,0.0004396192,0.002857671],"genre_scores_gemma":[0.925081,0.0005713279,0.06782374,0.0003254329,0.0001418472,0.0001640357,0.005355699,0.0000293517,0.0005076124],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005210428,"threshold_uncertainty_score":0.01520848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04972768180628666,"score_gpt":0.3502475645818436,"score_spread":0.300519882775557,"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."}}