{"id":"W4391116973","doi":"10.1609/aaaiss.v2i1.27716","title":"Predicting Individual Survival Distributions Using ECG: A Deep Learning Approach Utilizing Features Extracted by a Learned Diagnostic Model","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Symposium Series","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian VIGOUR Centre; University of Alberta","funders":"Canadian Institutes of Health Research; Alberta Innovates; Alberta Health Services","keywords":"Leverage (statistics); Machine learning; Artificial intelligence; Computer science; Predictive modelling; Margin (machine learning); Concordance; Personalized medicine; Population; Data mining; Medicine; Bioinformatics; Internal medicine","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":[0.001168585,0.0006395339,0.0005574559,0.0008975082,0.0001537539,0.0005829231,0.000780279,0.0006761363,0.0007461446],"category_scores_gemma":[0.003964028,0.0001959482,0.0004044558,0.0006167038,0.0002744557,0.0006196455,0.0007057031,0.001229756,0.000297746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000627052,"about_ca_system_score_gemma":0.0007359458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007958132,"about_ca_topic_score_gemma":0.01119335,"domain_scores_codex":[0.999774,0.00005987392,0.00001502986,0.00006709706,0.00004964466,0.00003426424],"domain_scores_gemma":[0.9987835,0.0007385132,0.0001175961,0.000115251,0.0001646178,0.00008037491],"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":[0.000426734,0.0003486116,0.07745552,0.00006165994,0.0001502695,0.0004058949,0.000117673,0.5471116,0.004274747,0.003104632,0.005771085,0.3607716],"study_design_scores_gemma":[0.000008920382,0.00004581555,0.003242178,0.000008642715,0.00001444075,0.00006026144,0.00001657281,0.9919406,0.0006527405,0.003656211,0.0003456759,0.000008039236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3418057,0.001051979,0.6497282,0.002249608,0.00009096385,0.00007141129,0.00194674,0.001144467,0.001911016],"genre_scores_gemma":[0.9528571,0.0003681889,0.04290875,0.0002959058,0.00008454252,0.00004336249,0.001838393,0.00003286507,0.001570809],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007958132,"threshold_uncertainty_score":0.01582366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02464475545182688,"score_gpt":0.2739643513970045,"score_spread":0.2493195959451777,"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."}}