{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001356299,0.0001820515,0.0003406716,0.00008198791,0.0001189856,0.00006659529,0.001774618,0.0001282466,0.0000588781],"category_scores_gemma":[0.001036823,0.0001379532,0.00007126202,0.000580353,0.0001629605,0.0004644813,0.0009861505,0.0006619699,0.000007048327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000437474,"about_ca_system_score_gemma":0.0006811004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007543457,"about_ca_topic_score_gemma":0.000008741958,"domain_scores_codex":[0.9936332,0.0003264071,0.001033657,0.0003224064,0.004421561,0.0002627849],"domain_scores_gemma":[0.9971402,0.0001472956,0.0002769455,0.00143246,0.0001899347,0.0008132188],"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":[0.0002162929,0.001752768,0.5818729,0.002133961,0.001026646,0.00007298758,0.03294717,0.1200219,3.717607e-7,0.002614503,0.0001360868,0.2572044],"study_design_scores_gemma":[0.000258766,0.000249602,0.0637959,0.0001011396,0.0000669209,0.000002071242,0.0005267221,0.934506,0.000004010948,0.0002991361,0.00006517625,0.0001245311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.495829,0.00001065869,0.5030501,0.0003695867,0.0001072787,0.0005065234,0.00003501355,0.00008251389,0.000009315721],"genre_scores_gemma":[0.9933865,0.000003547977,0.005693755,0.0005488177,0.0001542225,0.00001630909,0.0001859629,0.00001069228,1.890275e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8144841,"threshold_uncertainty_score":0.5625567,"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."}}