{"id":"W4297784739","doi":"10.2196/preprints.42540","title":"Using Decision Trees as an Expert System for Clinical Decision Support for COVID-19 (Preprint)","year":2022,"lang":"en","type":"preprint","venue":"","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Decision tree; Decision support system; Coronavirus disease 2019 (COVID-19); Preprint; Computer science; Clinical decision support system; Medicine; Data science; Intensive care medicine; Data mining; Disease; Infectious disease (medical specialty); Pathology; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007339081,0.0008446045,0.002280927,0.0007541814,0.0006079573,0.000236585,0.001114648,0.001167782,0.002142808],"category_scores_gemma":[0.01783646,0.0007748606,0.001803797,0.000269805,0.0001466816,0.0001545491,0.002486623,0.0009952008,0.00004414553],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.003947101,"about_ca_system_score_gemma":0.006247175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001559566,"about_ca_topic_score_gemma":0.0002579131,"domain_scores_codex":[0.9913253,0.0003685729,0.002865154,0.003221245,0.00140731,0.0008123791],"domain_scores_gemma":[0.9781756,0.01513218,0.0009285657,0.003579481,0.0006694841,0.001514644],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.04016811,0.007338856,0.02597852,0.009839078,0.001548571,0.0008420377,0.003339423,0.03952066,0.00113465,0.002987541,0.5304509,0.3368517],"study_design_scores_gemma":[0.01263381,0.003316932,0.001342733,0.002256843,0.001003456,0.0002941148,0.002373255,0.09330475,0.0007207205,0.004025375,0.8774036,0.001324483],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1869638,0.0004257339,0.785437,0.006468062,0.006740899,0.01216666,0.0003552926,0.001187694,0.0002549024],"genre_scores_gemma":[0.213457,0.00073679,0.6986447,0.07392285,0.003507199,0.005989817,0.001806755,0.0006386816,0.00129622],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3469526,"threshold_uncertainty_score":0.9998766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2503999910125183,"score_gpt":0.5273437044133756,"score_spread":0.2769437134008574,"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."}}