{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005577533,0.0002043791,0.0003548167,0.0002274974,0.0009744752,0.0000895781,0.00009776724,0.00005081298,0.0002591298],"category_scores_gemma":[0.0008141663,0.0002102043,0.00006615367,0.0004616832,0.0001175768,0.00009891547,0.0003632314,0.0006397422,0.000004225537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002721412,"about_ca_system_score_gemma":0.0001108079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006068465,"about_ca_topic_score_gemma":0.00002323693,"domain_scores_codex":[0.9983405,0.0001503697,0.0003820671,0.0005233335,0.0002633295,0.0003403258],"domain_scores_gemma":[0.9988545,0.0005128452,0.0001576962,0.00019176,0.00004262394,0.0002405353],"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":[0.000144587,0.0002950804,0.6510037,0.002863371,0.0001456252,0.0002607492,0.03133559,0.01564256,0.07615693,0.00006904962,0.000133025,0.2219497],"study_design_scores_gemma":[0.0002545263,0.0002115321,0.002204223,0.0002476065,0.0002468999,0.000334581,0.007244708,0.9694067,0.0111153,0.00005521458,0.008300602,0.0003781715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.978746,0.009273921,0.004819557,0.006544603,0.0000874008,0.0003367629,0.00001384477,0.0001490323,0.00002888849],"genre_scores_gemma":[0.9958721,0.00009300702,0.00106882,0.002403197,0.0001146628,0.00002229721,0.00004358333,0.00004092331,0.0003413959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9537641,"threshold_uncertainty_score":0.8571883,"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."}}