{"id":"W3124375572","doi":"10.18280/ria.340605","title":"An Automated Framework for Patient Identification and Verification Using Deep Learning","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Face recognition and analysis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Contrast (vision); Identification (biology); Face (sociological concept); Machine learning; Facial recognition system; Biometrics; Feature (linguistics); Identity (music); Feature extraction; Computer vision; Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000193829,0.0001007932,0.0001268831,0.0000720727,0.0002609748,0.0002568465,0.0002554239,0.00006527933,0.00002254072],"category_scores_gemma":[0.0002494241,0.0001093268,0.00005515802,0.0005273921,0.00003258379,0.0004039497,0.00004185656,0.0001083469,0.00006776907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002230532,"about_ca_system_score_gemma":0.00001456602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005618431,"about_ca_topic_score_gemma":8.354328e-7,"domain_scores_codex":[0.998877,0.00007470511,0.0003291656,0.0004398965,0.0001091076,0.0001700732],"domain_scores_gemma":[0.9992073,0.00009722639,0.0001613275,0.0002553229,0.0001475491,0.0001313037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001584011,0.0001272115,0.0002461508,0.00009949334,0.00002832166,0.000002500533,0.01167896,0.3097967,0.1058654,0.0292229,0.00002815688,0.5428883],"study_design_scores_gemma":[0.00001554593,0.00009200239,0.00003673238,0.00002305977,0.00001261802,0.000002869064,0.001093009,0.9353966,0.06101355,0.001772032,0.0004134446,0.0001284947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0622124,0.0001124912,0.9361417,0.000896861,0.00008024037,0.0001868252,0.000001776997,0.0003468493,0.00002081004],"genre_scores_gemma":[0.9064982,0.00004311411,0.09316094,0.0002074348,0.00003715372,0.00001844882,0.00001523513,0.000009348365,0.0000101093],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8442858,"threshold_uncertainty_score":0.4458219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05229975263533684,"score_gpt":0.3134222893284405,"score_spread":0.2611225366931037,"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."}}