{"id":"W2617001213","doi":"","title":"Section Heading Recognition in Electronic Health Records Using Conditional Random Fields.","year":2014,"lang":"en","type":"article","venue":"Taipei Medical University Repository","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Conditional random field; Named-entity recognition; Computer science; Section (typography); Medical diagnosis; Clinical decision support system; Artificial intelligence; Natural language processing; Health records; Domain (mathematical analysis); Medical record; Information retrieval; Heading (navigation); Machine learning; Masking (illustration); Decision support system; Data science; Health care; Medicine; Engineering","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":[0.002832593,0.0005419767,0.000805228,0.01091679,0.0005769905,0.001424719,0.001034623,0.001061371,0.004957468],"category_scores_gemma":[0.01699793,0.0002769494,0.0009653414,0.008509862,0.000307854,0.002327227,0.001212433,0.00088565,0.003415073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009646275,"about_ca_system_score_gemma":0.003776613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009203803,"about_ca_topic_score_gemma":0.01476184,"domain_scores_codex":[0.9981319,0.0003423833,0.0003846895,0.0005026687,0.0004916072,0.0001467063],"domain_scores_gemma":[0.9895086,0.006191681,0.001648071,0.001053532,0.001281607,0.0003163577],"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":[0.001335854,0.0006355598,0.09689065,0.002872312,0.0003695752,0.001542539,0.0006734925,0.01089537,0.01599726,0.01157752,0.1415749,0.715635],"study_design_scores_gemma":[0.0004622942,0.0007885361,0.153674,0.002460971,0.001245032,0.003783156,0.00151654,0.482461,0.06118538,0.06802577,0.2241212,0.000276083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.1622992,0.007913493,0.323048,0.004188801,0.0009427526,0.001750825,0.4371105,0.05311042,0.009636112],"genre_scores_gemma":[0.3257372,0.002236737,0.3059714,0.0006666003,0.0004073261,0.0009578021,0.3591825,0.0006019711,0.004238418],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01091679,"threshold_uncertainty_score":0.01830041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01149441006777359,"score_gpt":0.2415547741239862,"score_spread":0.2300603640562126,"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."}}