{"id":"W2751143095","doi":"10.2196/resprot.7757","title":"Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods","year":2017,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Data science; Big data; Artificial intelligence; Machine learning; Data mining","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":[0.005823575,0.00007673643,0.0002342274,0.0001075327,0.0006175151,0.0001057476,0.0002204105,0.0001026669,0.00002550449],"category_scores_gemma":[0.002479142,0.00005637848,0.0000198357,0.0001018102,0.0007318014,0.0003922851,0.0002165409,0.0007068905,0.000004589091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000310724,"about_ca_system_score_gemma":0.0009782212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003193525,"about_ca_topic_score_gemma":0.00007316656,"domain_scores_codex":[0.9978307,0.0006865907,0.000474509,0.0003331939,0.0004428284,0.0002321185],"domain_scores_gemma":[0.9976348,0.0005598075,0.000262187,0.0007221597,0.0006733463,0.0001477585],"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":[0.0006953261,0.0002109632,0.1727247,0.0007448569,0.00002287238,0.000003959812,0.0004669452,0.000008515401,0.0009349525,0.002166897,0.0001636794,0.8218563],"study_design_scores_gemma":[0.003207032,0.01635457,0.1269642,0.01604942,0.00005893085,0.0004367446,0.005915488,0.6603126,0.05046216,0.05084667,0.0685973,0.0007949085],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"protocol","genre_gemma":"protocol","genre_scores_codex":[0.1219582,0.00004488339,0.04051489,0.009590745,0.00009890784,0.8242909,0.00002824408,0.0001355471,0.003337625],"genre_scores_gemma":[0.1068383,0.00002270432,0.3277556,0.00003661369,0.001166176,0.5633352,0.00008747958,0.00004031488,0.0007175911],"genre_candidate":"protocol","genre_consensus":"protocol","teacher_disagreement_score":0.8210614,"threshold_uncertainty_score":0.4749489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8694162831942269,"score_gpt":0.7297005020872908,"score_spread":0.1397157811069362,"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."}}