{"id":"W2397665446","doi":"10.3233/978-1-61499-289-9-594","title":"Engineering Natural Language Processing Solutions for Structured Information from Clinical Text: Extracting Sentinel Events from Palliative Care Consult Letters","year":2013,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Topic Modeling","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Information extraction; Terminology; Artificial intelligence; Natural language processing; Natural language; SNOMED CT; Data extraction; Event (particle physics); Information retrieval; Bridge (graph theory); Machine learning; Medicine; MEDLINE; Linguistics","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":[0.005706878,0.001183517,0.0008886238,0.007782974,0.0012656,0.003395617,0.00111593,0.001260707,0.002047128],"category_scores_gemma":[0.02530567,0.0005743979,0.001673965,0.005244376,0.000936418,0.003267893,0.002277332,0.001386592,0.002151666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001131874,"about_ca_system_score_gemma":0.003971802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002899009,"about_ca_topic_score_gemma":0.003398309,"domain_scores_codex":[0.9937395,0.002710968,0.001252728,0.0009427859,0.00117796,0.000176021],"domain_scores_gemma":[0.9658712,0.02650253,0.002417552,0.00144893,0.003516868,0.000242949],"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":[0.0004388116,0.0003948625,0.01383263,0.005682822,0.0002485659,0.004039025,0.01053345,0.03059261,0.045199,0.02035833,0.0187093,0.8499707],"study_design_scores_gemma":[0.0002765842,0.0005361395,0.01726019,0.00129851,0.0006415149,0.004539319,0.01731898,0.586567,0.08809356,0.1284867,0.1546137,0.0003678002],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04191687,0.0006359608,0.9427845,0.001626567,0.0001172625,0.001473179,0.0069361,0.002636536,0.001873098],"genre_scores_gemma":[0.06962831,0.0004745326,0.9178529,0.0001520302,0.00008440106,0.0008149285,0.01016558,0.0001619366,0.00066546],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007782974,"threshold_uncertainty_score":0.03018123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04325334462687584,"score_gpt":0.3589841908514617,"score_spread":0.3157308462245859,"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."}}