{"id":"W2889346582","doi":"10.2196/11897","title":"Prospective Real-World Performance Evaluation of a Machine Learning Algorithm to Predict 30-Day Readmissions in Patients with Heart Failure Using Electronic Medical Record Data","year":2018,"lang":"en","type":"article","venue":"Iproceedings","topic":"Heart Failure Treatment and Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Overfitting; Receiver operating characteristic; Machine learning; Heart failure; Artificial intelligence; Medical record; Medicine; Computer science; Algorithm; Intensive care medicine; Internal medicine; Artificial neural network","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":[0.001629707,0.0001973999,0.0003514579,0.0003456633,0.0001280016,0.00001620785,0.0001598904,0.00007605951,0.0004694274],"category_scores_gemma":[0.0004464116,0.0001489286,0.00002440952,0.0008098497,0.00006990031,0.0003024994,0.0001741441,0.0003325183,0.00002032628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004589988,"about_ca_system_score_gemma":0.0004638674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004191835,"about_ca_topic_score_gemma":0.0005386948,"domain_scores_codex":[0.9974139,0.00004225931,0.0003465131,0.0005158852,0.001257326,0.0004241693],"domain_scores_gemma":[0.9987654,0.0000270415,0.0001158742,0.0002842611,0.0005739806,0.0002334096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002388489,0.0002865919,0.9684083,0.00004502913,0.00007608751,0.000001134599,0.0005698931,0.000003450292,0.0000885733,0.00001574818,0.002166429,0.02809994],"study_design_scores_gemma":[0.006427314,0.004542382,0.8135814,0.00150648,0.000465439,0.000004981925,0.0002102107,0.1491718,0.0006554505,0.00002449882,0.02313326,0.0002768151],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955396,0.00001487909,0.0001993085,0.001555922,0.0000480918,0.001643369,0.000008253677,0.00006907116,0.0009215064],"genre_scores_gemma":[0.9904324,0.00001498066,0.008815172,0.000108753,0.0001762006,0.00005452313,0.0001178425,0.00003345992,0.0002466105],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1548269,"threshold_uncertainty_score":0.6073133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02664280493894529,"score_gpt":0.3161027577462273,"score_spread":0.289459952807282,"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."}}