{"id":"W4200250907","doi":"10.1101/2021.12.07.21267367","title":"Diagnosis of COVID-19 Using CT image Radiomics Features: A Comprehensive Machine Learning Study Involving 26,307 Patients","year":2021,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Artificial intelligence; Coronavirus disease 2019 (COVID-19); Logistic regression; Normalization (sociology); Voxel; Pattern recognition (psychology); Univariate; Support vector machine; Computer science; Dimensionality reduction; Pneumonia; Medicine; Machine learning; Multivariate statistics; Internal medicine","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":[0.001961193,0.0006301732,0.0008935538,0.0009531688,0.0003325446,0.0006283607,0.0006466077,0.0006190646,0.0006483523],"category_scores_gemma":[0.00398217,0.0003440104,0.0007233094,0.0005999343,0.0005161859,0.000605314,0.0006359642,0.0005291654,0.0003744237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005552904,"about_ca_system_score_gemma":0.0005255681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002320571,"about_ca_topic_score_gemma":0.002078461,"domain_scores_codex":[0.9990962,0.0002429268,0.0001029351,0.0003479882,0.0001315533,0.00007846168],"domain_scores_gemma":[0.9980348,0.0008685968,0.0002690349,0.0004523427,0.0002143773,0.0001609216],"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":[0.0006791621,0.0003390128,0.9634052,0.0000561587,0.0002557373,0.0005741603,0.0001393757,0.006754754,0.002636522,0.00006567244,0.001099427,0.02399482],"study_design_scores_gemma":[0.000113384,0.001168464,0.8805429,0.0000512239,0.0002697544,0.003174522,0.0005530691,0.1063875,0.00548476,0.0003941061,0.001787541,0.00007285753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968433,0.0001308907,0.001810588,0.00004601492,0.000005392353,0.00003198596,0.0009787085,0.00003290621,0.0001202303],"genre_scores_gemma":[0.9947806,0.00008004696,0.001743045,0.00002822757,0.00001251736,0.00003545125,0.003231529,0.000008562251,0.00007991829],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002320571,"threshold_uncertainty_score":0.01037186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03275712200630746,"score_gpt":0.3326070462297439,"score_spread":0.2998499242234364,"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."}}