{"id":"W2891676555","doi":"10.1161/circheartfailure.118.005193","title":"Neural Networks for Prognostication of Patients With Heart Failure","year":2018,"lang":"en","type":"article","venue":"Circulation Heart Failure","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; Ted Rogers Centre for Heart Research; University Health Network","funders":"","keywords":"Receiver operating characteristic; Medicine; Artificial neural network; Heart failure; Area under the curve; Discriminative model; Predictive modelling; Statistics; Cardiology; Artificial intelligence; Internal medicine; Computer science; Mathematics","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":[0.002416142,0.001088848,0.0007046161,0.001205287,0.000219085,0.0006834548,0.0006631712,0.0007655947,0.00107815],"category_scores_gemma":[0.009527465,0.0002429547,0.0004143916,0.000673374,0.0002682022,0.0005917769,0.0005792456,0.0009823865,0.000253394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008046171,"about_ca_system_score_gemma":0.000674089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008847447,"about_ca_topic_score_gemma":0.006023488,"domain_scores_codex":[0.9993297,0.0003475464,0.00005204536,0.0001266669,0.00009041225,0.00005359248],"domain_scores_gemma":[0.9973739,0.001873111,0.000310581,0.00009782413,0.0002731674,0.00007132696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007757652,0.0002501965,0.06246031,0.0001833611,0.0005209061,0.0001497733,0.00006805908,0.7602758,0.0008025108,0.0009776336,0.002185464,0.1713501],"study_design_scores_gemma":[0.00001575878,0.00007756188,0.006272671,0.00003492999,0.0000400391,0.00002422037,0.00001105468,0.9913367,0.0002372158,0.001780188,0.000159367,0.00001032634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6304951,0.01725931,0.3391978,0.003462716,0.0004586669,0.0002808073,0.002553579,0.001683652,0.004608443],"genre_scores_gemma":[0.9780724,0.0008925843,0.01946116,0.0001148563,0.00008582217,0.0001027829,0.0005805271,0.00001573702,0.0006742059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008847447,"threshold_uncertainty_score":0.01759189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01080035285895821,"score_gpt":0.2384016899638454,"score_spread":0.2276013371048872,"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."}}