{"id":"W4380422322","doi":"10.2196/47862","title":"Improving an Electronic Health Record–Based Clinical Prediction Model Under Label Deficiency: Network-Based Generative Adversarial Semisupervised Approach","year":2023,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Artificial intelligence; Machine learning; Computer science; Support vector machine; Receiver operating characteristic; Scalability; Logistic regression; Data set; Supervised learning; Data mining; Artificial neural network; Database","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00519031,0.0003324176,0.0005300084,0.0002265708,0.0005509258,0.0001724187,0.001369088,0.0004988037,0.00001992956],"category_scores_gemma":[0.0004645189,0.0002975877,0.0001397256,0.001350633,0.0001520357,0.0007516794,0.0003008017,0.0018522,0.00003814651],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003934313,"about_ca_system_score_gemma":0.006170523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001054221,"about_ca_topic_score_gemma":0.00005240414,"domain_scores_codex":[0.994017,0.0007278282,0.001734905,0.0005080262,0.001661433,0.001350813],"domain_scores_gemma":[0.9968177,0.00054113,0.0005698506,0.0009586504,0.0001952964,0.000917382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008548864,0.0005042392,0.002525407,0.0006892207,0.00002816037,0.000004724427,0.003873947,0.8388484,0.000003333785,0.01255553,0.008128345,0.1327532],"study_design_scores_gemma":[0.001916108,0.0008995034,0.0004554958,0.00007096679,0.000007348169,0.000004390186,0.0001960751,0.9951724,0.00000165013,0.0006604805,0.0003510358,0.0002644897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03754252,0.00003286995,0.9563844,0.002889319,0.0007249489,0.0008788514,0.00001411583,0.001255003,0.0002779372],"genre_scores_gemma":[0.4954033,0.00006400924,0.4721249,0.02963826,0.001368988,0.000407392,0.0008354962,0.00008013213,0.00007749342],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4842596,"threshold_uncertainty_score":0.9999476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05020924089187065,"score_gpt":0.3597824901316554,"score_spread":0.3095732492397847,"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."}}