{"id":"W4283030324","doi":"10.1101/2022.06.15.22276090","title":"Two-Step Machine Learning to Diagnose and Predict Involvement of Lungs in COVID-19 and Pneumonia using CT Radiomics","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Random forest; Artificial intelligence; Feature selection; Computer science; Machine learning; Support vector machine; Correlation; Majority rule; Radiomics; Oversampling; Pattern recognition (psychology); Histogram; Pneumonia; Decision tree; Feature (linguistics); Medicine; Mathematics; Image (mathematics)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002302394,0.00039674,0.001006452,0.0006759159,0.0001488763,0.00005529905,0.0002404219,0.00009782647,0.0001060768],"category_scores_gemma":[0.002499207,0.000390872,0.00010582,0.0003185516,0.0002026783,0.00005127635,0.001487421,0.002178934,4.611612e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004137136,"about_ca_system_score_gemma":0.0003796594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005056806,"about_ca_topic_score_gemma":0.0001310051,"domain_scores_codex":[0.9970642,0.0003583945,0.0007597213,0.0008346047,0.0005665989,0.0004164427],"domain_scores_gemma":[0.9980232,0.0004212414,0.0003427406,0.0004534057,0.00004088247,0.000718507],"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":[0.0001405181,0.0001257306,0.9676687,0.001662345,0.0001206727,0.0006105814,0.002109099,0.01700111,0.005027739,0.00005512444,0.00006750046,0.005410901],"study_design_scores_gemma":[0.008109076,0.00067158,0.1477209,0.002400037,0.0006166223,0.0006325523,0.001237467,0.8117868,0.0002672357,0.0003500164,0.02527803,0.0009296134],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886387,0.004868103,0.001452779,0.003471971,0.0003562005,0.001012306,0.00003263351,0.00006887384,0.00009844208],"genre_scores_gemma":[0.9876523,0.002869314,0.007070592,0.001891119,0.0001417171,0.00008067148,0.0001119191,0.00008744462,0.00009493177],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8199478,"threshold_uncertainty_score":0.9998543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0237422886722278,"score_gpt":0.3260068363250603,"score_spread":0.3022645476528325,"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."}}