{"id":"W4400453828","doi":"10.1016/j.ijcard.2024.132346","title":"Uncovering STEMI patient phenotypes using unsupervised machine learning","year":2024,"lang":"en","type":"letter","venue":"International Journal of Cardiology","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Medicine; Phenotype; Internal medicine; Artificial intelligence; Cardiology; Machine learning; Genetics; Gene","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":[0.001691171,0.0002698036,0.0005960405,0.000728058,0.0006762352,0.001336092,0.0005689161,0.006634218,0.001127657],"category_scores_gemma":[0.01824487,0.0002672368,0.0004860976,0.0003404365,0.0007840041,0.0009335317,0.0004593832,0.008416399,0.00152644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006106549,"about_ca_system_score_gemma":0.0007715424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001014034,"about_ca_topic_score_gemma":0.003120302,"domain_scores_codex":[0.9990169,0.0004516449,0.0001317038,0.00009667633,0.0002105869,0.00009254156],"domain_scores_gemma":[0.9875257,0.009837474,0.0003924183,0.0004528236,0.001263402,0.000528255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0004774433,0.0004060215,0.07171097,0.0001355237,0.0001340601,0.03466542,0.0008293742,0.00125396,0.005837634,0.009212435,0.5947645,0.2805726],"study_design_scores_gemma":[0.0006193598,0.0008610801,0.06338339,0.00070234,0.0003019437,0.1170368,0.002450136,0.09042516,0.007424334,0.2102315,0.5063282,0.0002356148],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.02876799,0.001322338,0.0116047,0.9447823,0.004733667,0.00006787071,0.0003143156,0.0003014145,0.008105471],"genre_scores_gemma":[0.409238,0.00372953,0.03130682,0.4879804,0.05360885,0.0002201322,0.0005021099,0.0002079812,0.01320607],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006634218,"threshold_uncertainty_score":0.008943915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1110065077617032,"score_gpt":0.3914721714439938,"score_spread":0.2804656636822906,"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."}}