{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007623764,0.0002083273,0.0005590615,0.00007694149,0.0001288671,0.00002751662,0.00009259267,0.0001936631,0.0001145524],"category_scores_gemma":[0.000514276,0.0001663027,0.00009998574,0.0003579967,0.00004907906,0.0001082102,0.00006314942,0.0005680315,0.00004725769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000235235,"about_ca_system_score_gemma":0.0009045193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003523814,"about_ca_topic_score_gemma":0.00001397699,"domain_scores_codex":[0.9973617,0.0001086131,0.001180203,0.0002250116,0.0007490275,0.0003754097],"domain_scores_gemma":[0.9980794,0.0001064464,0.0003142904,0.0002142418,0.00008510231,0.001200578],"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":[0.0004339096,0.002446329,0.6648762,0.004161668,0.0007365863,0.00003452891,0.005756098,0.0006000622,0.0002112865,0.0004078061,0.006733274,0.3136023],"study_design_scores_gemma":[0.002040596,0.00180985,0.04111144,0.0002177131,0.0001164191,0.00002232687,0.00009690411,0.9496113,0.0001826719,0.000006619327,0.004659519,0.0001246186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9811897,0.0003352039,0.00973974,0.006712857,0.00008497582,0.001623382,0.00002637318,0.0002548752,0.00003287473],"genre_scores_gemma":[0.9759712,0.0002694906,0.002664586,0.02029614,0.0002857055,0.0002146268,0.0002680248,0.00002491659,0.000005312525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9490113,"threshold_uncertainty_score":0.6781626,"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."}}