{"id":"W3012860294","doi":"10.1080/10255842.2020.1742709","title":"Blood transfusion prediction using restricted Boltzmann machines","year":2020,"lang":"en","type":"article","venue":"Computer Methods in Biomechanics & Biomedical Engineering","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Restricted Boltzmann machine; Task (project management); Identification (biology); Computer science; Blood transfusion; Artificial intelligence; Machine learning; Data mining; Medicine; Medical emergency; Pattern recognition (psychology); Deep learning; Engineering; Surgery","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":[0.000659911,0.0003108628,0.0004135134,0.0004049971,0.00008865152,0.0001253744,0.0008263494,0.0002227763,0.00001203022],"category_scores_gemma":[0.0001577052,0.0002965863,0.0001278837,0.002337788,0.00003036687,0.000356083,0.0004436563,0.0003887963,0.0000029679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005351692,"about_ca_system_score_gemma":0.00005296002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002499895,"about_ca_topic_score_gemma":3.168339e-7,"domain_scores_codex":[0.9975867,0.000279069,0.0005775672,0.0007195653,0.000361269,0.0004758439],"domain_scores_gemma":[0.9989297,0.0002227378,0.00008339178,0.0003553378,0.00005745842,0.000351307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007609049,0.00009172356,0.000007560047,0.00005536952,0.00004749091,0.00005652209,0.0003621762,0.01845132,0.771795,0.000814973,0.00007266679,0.2082376],"study_design_scores_gemma":[0.0005882519,0.0001936057,0.00005620207,0.00007441756,0.00002258469,0.00002399157,0.000003438053,0.9758583,0.0203055,0.0002550765,0.002347119,0.0002715135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002929213,0.0002874305,0.9935501,0.0007149639,0.001889521,0.0002242316,0.000008579434,0.0003894324,0.000006541055],"genre_scores_gemma":[0.03111998,0.00009473137,0.9676285,0.0003036973,0.0008015078,0.000009534741,0.00001109774,0.000029531,0.000001440185],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.957407,"threshold_uncertainty_score":0.9999486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02876329382135677,"score_gpt":0.2847331832428003,"score_spread":0.2559698894214436,"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."}}