{"id":"W3212899834","doi":"10.2196/32662","title":"Machine Learning–Based Hospital Discharge Prediction for Patients With Cardiovascular Diseases: Development and Usability Study","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Iran Telecommunication Research Center; Korea Health Industry Development Institute","keywords":"Usability; Computer science; Medicine; Medical emergency; Human–computer interaction","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":[0.0006405665,0.000184152,0.0003223184,0.0000567293,0.0003174256,0.000117729,0.0003288352,0.00008564821,0.00001595433],"category_scores_gemma":[0.0007191745,0.0001380961,0.00007816613,0.0002572278,0.00006864542,0.0003962633,0.0002770694,0.0003820736,0.000004163484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008369597,"about_ca_system_score_gemma":0.0004677181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009368639,"about_ca_topic_score_gemma":0.000006942027,"domain_scores_codex":[0.9973329,0.0001598402,0.0005496902,0.0002690272,0.001387826,0.0003006746],"domain_scores_gemma":[0.9984612,0.000191972,0.0001374302,0.0005149021,0.0002954381,0.000399061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001623433,0.0008347385,0.9363662,0.0005280215,0.0001079782,0.000005842625,0.007520541,0.0002677282,6.719617e-9,0.00009798859,0.00004042143,0.05421432],"study_design_scores_gemma":[0.003293642,0.001189321,0.5753408,0.0001034374,0.00003906295,0.000003126632,0.0007225784,0.4061695,0.000002760243,0.00002188322,0.01285164,0.0002622517],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7838514,0.0001005905,0.2139121,0.0003072679,0.0002001631,0.001378125,0.00002363968,0.0001960073,0.00003063756],"genre_scores_gemma":[0.9747325,0.000003709042,0.02445066,0.0001979045,0.00004258184,0.0003479742,0.0001969805,0.00001247653,0.00001518999],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4059018,"threshold_uncertainty_score":0.5631397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007607718083775431,"score_gpt":0.2546359990920832,"score_spread":0.2470282810083078,"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."}}