{"id":"W2995896384","doi":"10.1109/iemcon.2019.8936168","title":"Extending a Generative Adversarial Network to Produce Medical Records with Demographic Characteristics and Health System Use","year":2019,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Adversarial system; Computer science; Health records; Medical record; Generative grammar; Data science; Machine learning; Software; Data mining; Artificial intelligence; Computer security; Health care; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003800995,0.0006093128,0.0004856104,0.000617086,0.000268048,0.0007218482,0.001294966,0.0008457123,0.00294135],"category_scores_gemma":[0.01194682,0.0003803514,0.0007162525,0.0004804413,0.0006988748,0.001039606,0.001287007,0.001432782,0.0006238365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008689467,"about_ca_system_score_gemma":0.000689374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004558683,"about_ca_topic_score_gemma":0.005766829,"domain_scores_codex":[0.9990023,0.0005196213,0.00003857377,0.0001968887,0.0001721813,0.00007040551],"domain_scores_gemma":[0.9924763,0.005761908,0.0003546378,0.0007929248,0.0004895016,0.000124814],"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":[0.0001703443,0.00008692621,0.005739716,0.00005528539,0.0001098325,0.0001756901,0.000138866,0.9419255,0.001285313,0.009917686,0.002857314,0.0375375],"study_design_scores_gemma":[0.00000929759,0.00002433529,0.0002943623,0.000009024287,0.000008380925,0.00004118977,0.000006007894,0.9940327,0.0008267051,0.003925448,0.0008140648,0.000008534722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08176257,0.0002126688,0.9098083,0.001730551,0.000230017,0.000264577,0.001183313,0.001423102,0.003384959],"genre_scores_gemma":[0.8015417,0.0002086545,0.1871427,0.0008884598,0.000114284,0.0003091456,0.002467921,0.000177645,0.007149447],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004558683,"threshold_uncertainty_score":0.02010179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02074653823898131,"score_gpt":0.2828453880635338,"score_spread":0.2620988498245524,"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."}}