{"id":"W4415956882","doi":"10.1002/ejhf.70076","title":"Unsupervised Machine Learning for Cardiovascular Disease: A Framework for Future Studies","year":2025,"lang":"en","type":"review","venue":"European Journal of Heart Failure","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre","funders":"Agence Nationale de la Recherche; Université de Lorraine","keywords":"Cluster analysis; Unsupervised learning; Clinical Practice; Risk stratification; Personalized medicine; Predictive modelling; Resource (disambiguation)","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":["metaresearch","metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.006180878,0.0006526203,0.003653736,0.0004337601,0.001665252,0.00003479385,0.0007308588,0.0003546915,0.00007007516],"category_scores_gemma":[0.008451364,0.0004772213,0.004547236,0.0004715264,0.0000863292,0.0001218326,0.0002300783,0.003991888,0.00009315349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003228643,"about_ca_system_score_gemma":0.001832397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004876291,"about_ca_topic_score_gemma":0.00001224167,"domain_scores_codex":[0.9889083,0.006403426,0.002756434,0.0005579483,0.0005333949,0.0008405336],"domain_scores_gemma":[0.9903404,0.00520668,0.001358623,0.0007715339,0.001871501,0.000451327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002625731,0.00005732906,0.0002447524,0.2406274,0.004758806,0.0001436834,0.002237896,0.0001511626,6.130016e-8,0.001534158,0.09813005,0.6518521],"study_design_scores_gemma":[0.000238904,0.0002622191,0.000002096699,0.09198125,0.002940036,0.00001550629,0.003351778,0.00002092181,6.006515e-8,0.0005792988,0.9002794,0.0003285128],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000003268671,0.975071,0.009421633,0.006816274,0.004398127,0.003981604,0.0002225304,0.0000687742,0.00001681397],"genre_scores_gemma":[0.000009920066,0.9584038,0.03002166,0.0004243359,0.009866937,0.0002441215,0.00008002541,0.0002171563,0.0007320935],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8021494,"threshold_uncertainty_score":0.9999009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2070748629387882,"score_gpt":0.4894961708952026,"score_spread":0.2824213079564144,"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."}}