{"id":"W3045837041","doi":"10.2196/21798","title":"AutoScore: A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":143,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Medical Research Council; Medical Research Council","keywords":"Health records; Clinical decision support system; Medical record; Electronic health record; Computer science; Risk stratification; Electronic medical record; Machine learning; Health care; Generator (circuit theory); Artificial intelligence; Data mining; Medicine; Medical emergency; Decision support system; Internal medicine","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":[0.005436106,0.00102728,0.0007469135,0.002633536,0.000257591,0.001086777,0.001337389,0.0005991717,0.003898959],"category_scores_gemma":[0.01862403,0.0004275036,0.0008371266,0.001475356,0.0003598378,0.001144693,0.001476225,0.0009685212,0.001415698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005524631,"about_ca_system_score_gemma":0.001363607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002105215,"about_ca_topic_score_gemma":0.002570317,"domain_scores_codex":[0.9980736,0.0007583943,0.0002150666,0.0003879144,0.0004990193,0.00006594144],"domain_scores_gemma":[0.992361,0.004648344,0.0006472496,0.0007848491,0.001320762,0.0002377648],"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":[0.000951884,0.0004338917,0.06000945,0.0003451117,0.0003587318,0.0004650375,0.0002096407,0.083432,0.004769949,0.004319014,0.03992948,0.8047759],"study_design_scores_gemma":[0.0001985451,0.0003706404,0.01342244,0.00006151919,0.00006696799,0.0004177576,0.00004195732,0.9628322,0.00796868,0.007007692,0.007533972,0.00007756933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07693839,0.000280443,0.8665345,0.0007082795,0.0001415639,0.000866073,0.006687415,0.04632247,0.001520844],"genre_scores_gemma":[0.352432,0.0002369649,0.6309018,0.0003065036,0.0001492828,0.00102723,0.01237893,0.0009655033,0.001601788],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005436106,"threshold_uncertainty_score":0.02874923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1200558269226375,"score_gpt":0.4225546257764097,"score_spread":0.3024987988537723,"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."}}