{"id":"W4318195334","doi":"10.1186/s12911-023-02117-3","title":"Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach","year":2023,"lang":"en","type":"article","venue":"BMC Medical Informatics and Decision Making","topic":"Topic Modeling","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Public Health Ontario","funders":"Institute of Health Services and Policy Research; Canadian Institutes of Health Research","keywords":"Coronavirus disease 2019 (COVID-19); Health informatics; Computer science; Relation (database); Natural language processing; 2019-20 coronavirus outbreak; Information extraction; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Relationship extraction; Natural language; Artificial intelligence; Medicine; Data mining; Pathology; Public health","routes":{"ca_aff":true,"ca_fund":true,"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":[0.006026203,0.001479584,0.001015972,0.01609316,0.0009552817,0.003721942,0.001888278,0.001702822,0.003266952],"category_scores_gemma":[0.02211102,0.0006348311,0.002397446,0.0071959,0.0008252191,0.003606129,0.002601446,0.001680287,0.00258494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001123916,"about_ca_system_score_gemma":0.003243356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003844499,"about_ca_topic_score_gemma":0.00561336,"domain_scores_codex":[0.9948761,0.001338712,0.001337077,0.001226005,0.001054951,0.000167254],"domain_scores_gemma":[0.9750267,0.01718937,0.002959427,0.002135669,0.002256036,0.0004328164],"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":[0.0005692678,0.0007354406,0.04357887,0.006422333,0.0007570285,0.009608016,0.003047882,0.01536696,0.04971684,0.01244231,0.05368128,0.8040737],"study_design_scores_gemma":[0.0003160456,0.000614876,0.08758153,0.002803503,0.001970512,0.01793173,0.007362391,0.4166954,0.1114428,0.08162569,0.2711283,0.0005272478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06856008,0.005497994,0.8314996,0.005161807,0.0004359019,0.002969513,0.06299163,0.01720344,0.005680041],"genre_scores_gemma":[0.1370546,0.002103656,0.7854437,0.000457963,0.0003234518,0.00102032,0.07173539,0.0003160807,0.001544891],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01609316,"threshold_uncertainty_score":0.03187001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08397814884412258,"score_gpt":0.4160037932922904,"score_spread":0.3320256444481678,"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."}}