{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007017052,0.0009860407,0.0002926831,0.002774082,0.0002386991,0.001525762,0.0006200779,0.0007087265,0.03609281],"category_scores_gemma":[0.004607853,0.0002396595,0.0004409956,0.001598519,0.0001973827,0.001049113,0.0006416928,0.0004942665,0.004784565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002961276,"about_ca_system_score_gemma":0.0004459182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001844339,"about_ca_topic_score_gemma":0.001677933,"domain_scores_codex":[0.9996897,0.0001031913,0.00005086736,0.00006310979,0.00006699275,0.00002612267],"domain_scores_gemma":[0.9969993,0.001870284,0.0002454362,0.000209373,0.0005729574,0.0001027478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003042341,0.0004207907,0.01064025,0.003511217,0.0001044491,0.002875024,0.004501631,0.009391492,0.07005326,0.0222911,0.3672252,0.5059434],"study_design_scores_gemma":[0.0003975697,0.0006994655,0.03847427,0.001416759,0.0002091777,0.003384806,0.00167344,0.1477,0.08051492,0.01376005,0.711441,0.0003284697],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07133405,0.002080487,0.6741716,0.004287989,0.001308656,0.001399591,0.08770634,0.1141044,0.04360685],"genre_scores_gemma":[0.2472111,0.001832593,0.6895918,0.001507087,0.0004725176,0.001137084,0.03195631,0.004738599,0.02155287],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03609281,"threshold_uncertainty_score":0.1207425,"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."}}