{"id":"W2886305294","doi":"10.1109/civemsa.2018.8439958","title":"Pictorial Visualization of EMR Summary Interface and Medical Information Extraction of Clinical Notes","year":2018,"lang":"en","type":"article","venue":"","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; University of Ottawa","funders":"","keywords":"Computer science; Timeline; Information extraction; Visualization; Interface (matter); Information retrieval; User interface; Graphical user interface; Representation (politics); Human–computer interaction; Information visualization; Artificial intelligence; Natural language processing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004450075,0.00004881422,0.0001210144,0.000025431,0.00001579293,0.000003865048,0.00006283459,0.0002610008,0.00005418037],"category_scores_gemma":[0.001874701,0.00003796487,0.0000304116,0.00004217782,0.0003349231,0.000006508912,0.00006411839,0.00004270833,0.000002017618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001487927,"about_ca_system_score_gemma":0.00005332711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002971942,"about_ca_topic_score_gemma":0.00002535739,"domain_scores_codex":[0.9992597,0.00004995309,0.0003996147,0.00008534268,0.0001452909,0.00006017354],"domain_scores_gemma":[0.9995252,0.00006803213,0.0001451777,0.00008948959,0.0001250272,0.00004700204],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001244853,0.0002748643,0.07006427,0.0001565672,0.0001383085,4.339213e-7,0.000413363,9.660326e-7,0.1547845,0.001209757,0.01458152,0.7571306],"study_design_scores_gemma":[0.002344681,0.00393446,0.06449727,0.0001234042,0.00004038988,0.00001641333,0.0003899218,0.001504731,0.7720078,0.0001991751,0.1547079,0.0002338539],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8975532,0.0001140851,0.09987542,0.0001155206,0.000669133,0.00004941914,0.000003987554,0.00001071786,0.00160851],"genre_scores_gemma":[0.9977877,0.0001941201,0.001503173,0.00006583946,0.0003763987,9.580793e-7,0.0000329205,0.00000236175,0.00003651942],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7568967,"threshold_uncertainty_score":0.2244328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02728687747125595,"score_gpt":0.4015245338604745,"score_spread":0.3742376563892186,"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."}}