{"id":"W3206851169","doi":"10.1145/3469035","title":"Differentially Private Medical Texts Generation Using Generative Neural Networks","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Computing for Healthcare","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina; University of Manitoba","funders":"National Institutes of Health; University of Texas Health Science Center at Houston","keywords":"Computer science; Generative grammar; Medical record; Health care; Data science; Information retrieval; Health records; Big data; Private information retrieval; Patient care; Volume (thermodynamics); Artificial intelligence; Data mining; Medicine; Nursing; Computer security","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":[0.001618854,0.0006782755,0.0007351317,0.0009190728,0.0004660855,0.0008611505,0.001433331,0.001280141,0.00298561],"category_scores_gemma":[0.008137534,0.000360203,0.0008087654,0.0006898465,0.00100957,0.001124788,0.001402899,0.001356863,0.001047675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001323394,"about_ca_system_score_gemma":0.0008713724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002976314,"about_ca_topic_score_gemma":0.004297778,"domain_scores_codex":[0.9988839,0.0004496192,0.00005293892,0.0002685088,0.0002439679,0.0001010432],"domain_scores_gemma":[0.9936633,0.004852663,0.0002525019,0.0006294709,0.0004915737,0.0001104217],"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":[0.0005282412,0.0002258974,0.002763131,0.0001881495,0.0000687177,0.0004883531,0.0002616535,0.7545453,0.006996023,0.02133787,0.006929297,0.2056675],"study_design_scores_gemma":[0.00001606866,0.00001905353,0.0001155088,0.000005594265,0.000005077306,0.00003472172,0.00001109717,0.9909409,0.001252899,0.007109184,0.0004847372,0.000005157985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1125198,0.0008232042,0.8744686,0.001680307,0.0002946171,0.000351141,0.001387071,0.002387408,0.00608791],"genre_scores_gemma":[0.8238971,0.0002684693,0.1627464,0.0005383187,0.0001975371,0.0003544826,0.003267496,0.0002347861,0.008495454],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00298561,"threshold_uncertainty_score":0.009987831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06312692867739636,"score_gpt":0.3529737858752012,"score_spread":0.2898468571978049,"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."}}