{"id":"W2789405763","doi":"10.1093/ehjci/jey041","title":"Machine learning for predictive analytics in medicine: real opportunity or overblown hype?","year":2018,"lang":"en","type":"letter","venue":"European Heart Journal - Cardiovascular Imaging","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children; Ted Rogers Centre for Heart Research; University Health Network; University of Toronto","funders":"","keywords":"Predictive analytics; Analytics; Computer science; Data science; Machine learning; Artificial intelligence","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01704726,0.0006733878,0.001674501,0.000865172,0.00224664,0.00722175,0.002190059,0.03165175,0.01149855],"category_scores_gemma":[0.08178553,0.0005389816,0.0008530213,0.0007239321,0.008541402,0.0132498,0.003399619,0.0511024,0.007299322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002421963,"about_ca_system_score_gemma":0.003790183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001672647,"about_ca_topic_score_gemma":0.003578406,"domain_scores_codex":[0.9906706,0.004205136,0.0008568539,0.000914624,0.002715964,0.0006367492],"domain_scores_gemma":[0.8982378,0.07822455,0.002911735,0.003005722,0.01043876,0.007181406],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000927902,0.00006984807,0.0006119217,0.0001835736,0.00004078821,0.000749858,0.0002495642,0.0001592244,0.0001525797,0.022338,0.9125195,0.06283245],"study_design_scores_gemma":[0.000169725,0.00008863344,0.000569425,0.001131759,0.00003090427,0.001315362,0.0009151672,0.00186766,0.0001891693,0.1402106,0.8534232,0.00008850513],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.00009410161,0.00275659,0.0002871302,0.9918641,0.004297815,0.000002107168,0.00001182344,0.00001371883,0.0006727034],"genre_scores_gemma":[0.007800842,0.00955596,0.001926344,0.8651978,0.1118058,0.00003338174,0.00003267195,0.0000525193,0.003594609],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.9829527,"threshold_uncertainty_score":0.0901556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2210104158557306,"score_gpt":0.4041073159735836,"score_spread":0.183096900117853,"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."}}