{"id":"W2128795341","doi":"10.1109/cbms.2009.5255451","title":"Scenario-oriented information extraction from electronic health records","year":2009,"lang":"en","type":"article","venue":"","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Matching (statistics); Information retrieval; Electronic health record; Task (project management); Set (abstract data type); Health records; Information extraction; Rank (graph theory); Data mining; Health care; Data science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001229581,0.00007563088,0.00008035697,0.00002773837,0.00006262723,0.0000150116,0.00006762582,0.0001209127,0.00004916681],"category_scores_gemma":[0.0000676717,0.0000638537,0.00003513812,0.00006440184,0.00002171071,0.000006558656,0.00001279359,0.00009352795,0.00003042015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003033865,"about_ca_system_score_gemma":0.0001115122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001983514,"about_ca_topic_score_gemma":0.00009700385,"domain_scores_codex":[0.9993693,0.00002721433,0.0001702831,0.0001266088,0.00008527095,0.0002213418],"domain_scores_gemma":[0.9996809,0.000005704946,0.0000710097,0.0001499818,0.00003091291,0.0000615106],"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":[0.0001133472,0.00006208307,0.0005396458,0.000003153901,0.00002047641,3.042085e-7,0.00009253963,0.000004453018,0.02138038,0.0004709744,0.03377283,0.9435398],"study_design_scores_gemma":[0.0005283675,0.00125687,0.01858154,0.000009634201,0.000004549893,0.000007226267,0.0002227464,0.0001964246,0.01765076,0.0005587118,0.960831,0.0001521089],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7980037,0.001477153,0.1838104,0.008402965,0.0005200086,0.0002196852,0.00001948666,0.0001699352,0.007376655],"genre_scores_gemma":[0.9902417,0.0004043418,0.004827075,0.003352719,0.0001560907,0.000003548519,0.0005366403,0.0000031565,0.0004747231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9433877,"threshold_uncertainty_score":0.2603878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007250516698653354,"score_gpt":0.2792959909849407,"score_spread":0.2720454742862874,"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."}}