{"id":"W3095788424","doi":"10.1038/s41598-020-75544-1","title":"De-identification of electronic health record using neural network","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Identification (biology); Process (computing); Health records; Compromise; Artificial neural network; Data mining; Mechanism (biology); Data anonymization; Artificial intelligence; Machine learning; Information sensitivity; Data science; Quality (philosophy); Information retrieval; Information privacy; Health care; 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":[0.001854361,0.0008541201,0.0009933785,0.002865598,0.0005592584,0.001079872,0.001429541,0.00112059,0.0007803738],"category_scores_gemma":[0.006853458,0.0002984181,0.0008423955,0.002075816,0.0003859254,0.00193472,0.001113202,0.001293106,0.0008591613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001005076,"about_ca_system_score_gemma":0.0009201979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006396213,"about_ca_topic_score_gemma":0.006727273,"domain_scores_codex":[0.9976783,0.0003696042,0.0003246875,0.0006702612,0.0007186808,0.0002384707],"domain_scores_gemma":[0.9968407,0.0009826381,0.0005920632,0.0004019015,0.001121874,0.00006087593],"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":[0.0006184581,0.0004204933,0.01372812,0.0003044769,0.0002293055,0.0004895767,0.0002614487,0.07849204,0.01669853,0.003922083,0.007966218,0.8768692],"study_design_scores_gemma":[0.00001029353,0.00005787561,0.003358508,0.00003088315,0.00006395911,0.0001934797,0.000045469,0.9777429,0.0138051,0.002604123,0.002061104,0.00002620784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1374973,0.003134748,0.8483198,0.001154519,0.0004655904,0.0002461861,0.001397433,0.003870195,0.003914259],"genre_scores_gemma":[0.7358666,0.001412375,0.248395,0.0005803763,0.0004102224,0.0001456939,0.004792212,0.00009891651,0.008298635],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006396213,"threshold_uncertainty_score":0.01271796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03266138406027824,"score_gpt":0.3162253936720649,"score_spread":0.2835640096117866,"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."}}