{"id":"W4391934252","doi":"10.1016/j.ijmedinf.2024.105384","title":"hART: Deep learning-informed lifespan heart failure risk trajectories","year":2024,"lang":"en","type":"article","venue":"International Journal of Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre","funders":"Fonds de Recherche du Québec - Santé; Heart And Stroke Foundation Of Quebec; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Heart and Stroke Foundation of Canada","keywords":"Medicine; Heart failure; Recall; Cohort; Psychological intervention; Heart disease; Framingham Risk Score; Disease; Cardiology; Internal medicine; Psychology; Cognitive psychology; Psychiatry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.00077605,0.0009187225,0.0005147394,0.000659884,0.000230698,0.0004866635,0.0009145405,0.0007852516,0.001727821],"category_scores_gemma":[0.002690619,0.0002772525,0.0005065485,0.0004234799,0.0002109561,0.0004936911,0.0008744715,0.001147568,0.0004335013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000936178,"about_ca_system_score_gemma":0.001196825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04210416,"about_ca_topic_score_gemma":0.0569372,"domain_scores_codex":[0.9998037,0.00005625327,0.00001051129,0.00006273243,0.0000257505,0.00004112062],"domain_scores_gemma":[0.9994198,0.0002874632,0.00006849698,0.000066996,0.00008757989,0.00006972776],"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":[0.0008552144,0.0007663015,0.1609246,0.0002034944,0.0006367468,0.0005289299,0.0001754479,0.5090477,0.001687585,0.002227972,0.04244324,0.2805027],"study_design_scores_gemma":[0.00004937547,0.0001045336,0.007307913,0.00002543632,0.00003668934,0.00005853923,0.00002402695,0.9874001,0.0004726123,0.003029771,0.001474779,0.00001637613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6536105,0.004020018,0.2949323,0.00403565,0.0003790251,0.0003613583,0.02952777,0.009003645,0.004129741],"genre_scores_gemma":[0.9499575,0.0004161822,0.03217303,0.0006479836,0.0001028023,0.0001788926,0.0130872,0.00008535933,0.003350871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04210416,"threshold_uncertainty_score":0.08371818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01185906906088453,"score_gpt":0.3288418832135166,"score_spread":0.3169828141526321,"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."}}