{"id":"W3112199150","doi":"10.2196/25000","title":"Mortality Prediction of Patients With Cardiovascular Disease Using Medical Claims Data Under Artificial Intelligence Architectures: Validation Study","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"La Trobe University","keywords":"Machine learning; Random forest; Artificial intelligence; Medicine; Decision tree; Artificial neural network; Computer science; Logistic regression; Gradient boosting; Oversampling; Health care; Data set; Mortality rate; Medical record; Data mining; Surgery","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0091176,0.001445323,0.0007325173,0.001295212,0.0004551492,0.0006710693,0.001203342,0.001418653,0.0009389213],"category_scores_gemma":[0.01371069,0.0003396609,0.001500473,0.0007311427,0.0008345387,0.0008385405,0.0009869477,0.001623497,0.0004177303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001013094,"about_ca_system_score_gemma":0.00102793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008792817,"about_ca_topic_score_gemma":0.004867672,"domain_scores_codex":[0.9980027,0.001087035,0.0001498758,0.0002793466,0.0003085919,0.0001723335],"domain_scores_gemma":[0.9890538,0.00637291,0.0007831736,0.001372948,0.001967478,0.0004497234],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006052111,0.007946989,0.5728312,0.0004635159,0.001894087,0.0004088936,0.0004532186,0.3135501,0.003200791,0.001164778,0.006206434,0.08582788],"study_design_scores_gemma":[0.0003171869,0.003567785,0.1569751,0.00007916586,0.0002465171,0.0001895141,0.0002203992,0.8344026,0.002338126,0.0007209522,0.0008894938,0.00005312288],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965498,0.0002227044,0.001885439,0.0001309745,0.00003376073,0.00007675948,0.0006461094,0.00005844429,0.0003960098],"genre_scores_gemma":[0.9936705,0.0001258377,0.002723233,0.00005140728,0.00002933107,0.00006465147,0.003042947,0.000005617019,0.0002863644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0091176,"threshold_uncertainty_score":0.04821908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1105199280102903,"score_gpt":0.3586939151534375,"score_spread":0.2481739871431472,"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."}}