{"id":"W2910814369","doi":"10.1371/journal.pone.0208737","title":"Computational prediction of diagnosis and feature selection on mesothelioma patient health records","year":2019,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Occupational and environmental lung diseases","field":"Medicine","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Princess Margaret Cancer Centre","funders":"University of California, Irvine; University of Toronto; University of Melbourne","keywords":"Feature selection; Mesothelioma; Feature (linguistics); Computer science; Health records; Computational biology; Medicine; Bioinformatics; Artificial intelligence; Data mining; Biology; Pathology; Health care","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0021094,0.0005724093,0.0006090852,0.001686549,0.0002801052,0.0007120177,0.0005304744,0.0005891189,0.0009300187],"category_scores_gemma":[0.009328499,0.0001566089,0.000661024,0.0009324765,0.0002145325,0.0004577418,0.0003746886,0.0006770196,0.000270367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007563211,"about_ca_system_score_gemma":0.0009176998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007614589,"about_ca_topic_score_gemma":0.005139155,"domain_scores_codex":[0.999124,0.0003272725,0.00009745059,0.0002063263,0.0001538048,0.00009117518],"domain_scores_gemma":[0.9899574,0.008204443,0.0006258083,0.000326159,0.0006951072,0.000191138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001381804,0.0008161058,0.2716899,0.0002648285,0.0003369771,0.0006054835,0.0001629507,0.5363817,0.002233821,0.0005140566,0.004704321,0.1809081],"study_design_scores_gemma":[0.00001797261,0.0001089334,0.01909959,0.00001518862,0.00002816636,0.00009984796,0.00003990745,0.9787716,0.001037264,0.0005418791,0.0002315253,0.000008037088],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.95568,0.0005672444,0.03809347,0.0008234732,0.00005902851,0.0001365654,0.003183408,0.0007535083,0.0007033611],"genre_scores_gemma":[0.981979,0.00009052204,0.01479135,0.00004429503,0.00003950186,0.00005086492,0.002801315,0.000006150064,0.0001969651],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007614589,"threshold_uncertainty_score":0.01514053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02016829143078109,"score_gpt":0.231500363062056,"score_spread":0.2113320716312749,"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."}}