{"id":"W3090343440","doi":"10.1007/978-3-030-59713-9_56","title":"A Deep Bayesian Video Analysis Framework: Towards a More Robust Estimation of Ejection Fraction","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Vancouver General Hospital; University of British Columbia","funders":"","keywords":"Ejection fraction; Computer science; Bayesian probability; Artificial intelligence; Workflow; Probabilistic logic; Cardiac cycle; Baseline (sea); Population; Machine learning; Cardiology; Medicine","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":[0.001324238,0.0009209356,0.0009807807,0.000633247,0.0002278937,0.001190937,0.001297881,0.001365057,0.00276553],"category_scores_gemma":[0.00288244,0.000682051,0.0008411178,0.0006828393,0.0004509159,0.001028567,0.001295718,0.002011748,0.001539047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005352828,"about_ca_system_score_gemma":0.0008847787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00580753,"about_ca_topic_score_gemma":0.007368765,"domain_scores_codex":[0.9996232,0.000102744,0.00001742211,0.0001075309,0.0001103136,0.00003878301],"domain_scores_gemma":[0.9994392,0.0002702835,0.00005243925,0.00005794855,0.0001426579,0.00003757215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001483875,0.00008887397,0.0008328306,0.0001378329,0.000142689,0.00007217236,0.00005786637,0.1881307,0.03166858,0.02260592,0.01043726,0.7456769],"study_design_scores_gemma":[0.000006154076,0.00001953111,0.0003573803,0.00001979906,0.00001836411,0.0000491221,0.00000416983,0.9841568,0.002783675,0.01020905,0.002363686,0.00001224732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001189428,0.0003233667,0.9975783,0.0001148157,0.0000299479,0.000007036703,0.0001083598,0.000227191,0.0004216465],"genre_scores_gemma":[0.099176,0.001695631,0.8888534,0.0004749789,0.0003325998,0.00008860636,0.000895881,0.0003846465,0.008098265],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00580753,"threshold_uncertainty_score":0.01154745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01541054946351086,"score_gpt":0.3113363962237282,"score_spread":0.2959258467602173,"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."}}