{"id":"W4220653964","doi":"10.1016/s0735-1097(22)02288-4","title":"MACHINE LEARNING BASED PREDICTION OF CARDIAC-RELATED HOSPITALIZATION COSTS AT TIME OF DIAGNOSTIC IMAGING: DEMONSTRATION OF VALUE FROM MULTI-DOMAIN PHENOTYPIC DATA AT TIME OF CARDIOVASCULAR MRI","year":2022,"lang":"en","type":"article","venue":"Journal of the American College of Cardiology","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Medicine; Value (mathematics); Time domain; Artificial intelligence; Machine learning; Cardiology; Computer vision","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":[0.002153816,0.0005977779,0.000683459,0.001405957,0.0002048102,0.001113013,0.0007143925,0.0008319384,0.001004362],"category_scores_gemma":[0.01051464,0.0001634906,0.0005039693,0.0009361909,0.0002660463,0.0006982207,0.0004853802,0.001145986,0.0003413593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003597246,"about_ca_system_score_gemma":0.0005105535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007239539,"about_ca_topic_score_gemma":0.006571682,"domain_scores_codex":[0.9992319,0.0004143616,0.0000490985,0.000164263,0.00007176443,0.00006869355],"domain_scores_gemma":[0.9904397,0.007031712,0.0008347892,0.0006242684,0.0006440995,0.0004255444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001799215,0.0005255184,0.8778335,0.00005732099,0.0003332744,0.00028671,0.00006846073,0.06588131,0.00140704,0.0005887077,0.001235156,0.04998378],"study_design_scores_gemma":[0.00005350121,0.0004594977,0.3037516,0.00002936759,0.0001171937,0.0003759472,0.0001510847,0.6911138,0.001192679,0.002205184,0.0005125313,0.00003759355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9867501,0.000397266,0.00885469,0.0005614912,0.00004673049,0.00002093236,0.002251777,0.000108804,0.001008259],"genre_scores_gemma":[0.9960788,0.00009408988,0.002559172,0.00003580388,0.00003074249,0.000006699486,0.001019993,0.000009097027,0.0001656551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007239539,"threshold_uncertainty_score":0.01439482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008874518537991276,"score_gpt":0.2310611917081877,"score_spread":0.2221866731701964,"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."}}