{"id":"W4389577465","doi":"10.1109/ichi57859.2023.00103","title":"Leveraging Foundation Models for Clinical Text Analysis","year":2023,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Public Health Ontario; University of Toronto","funders":"","keywords":"Computer science; Transformer; Information extraction; Data extraction; Data science; Artificial intelligence; Machine learning; Data mining; Natural language processing; Information retrieval; MEDLINE; 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":[0.002387736,0.0008659295,0.0004719545,0.002895041,0.0004283949,0.00128587,0.001056227,0.0006989224,0.002444975],"category_scores_gemma":[0.009710636,0.0003368017,0.00129024,0.001839925,0.0004600256,0.003873267,0.001363831,0.001449294,0.002196227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008310585,"about_ca_system_score_gemma":0.002098264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006332687,"about_ca_topic_score_gemma":0.0109036,"domain_scores_codex":[0.9989234,0.0003439109,0.0001295151,0.0002963005,0.0002306462,0.00007613218],"domain_scores_gemma":[0.9953951,0.003142034,0.0003229998,0.0004093019,0.000630949,0.00009959551],"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":[0.0005565783,0.0003279991,0.01195436,0.0005934563,0.0002752515,0.0007078962,0.0006579797,0.0893062,0.01555086,0.02800664,0.02159625,0.8304664],"study_design_scores_gemma":[0.00003457154,0.00009355026,0.002348606,0.00006632764,0.0001051941,0.0002553848,0.0001238179,0.9376125,0.006614938,0.04007997,0.01262968,0.00003553844],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01878197,0.0007682585,0.9718911,0.0007737222,0.00008958842,0.000227452,0.001980938,0.00354235,0.00194458],"genre_scores_gemma":[0.5366688,0.001678893,0.4414805,0.0005050306,0.0003193002,0.0005176899,0.014103,0.0004396792,0.004287098],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006332687,"threshold_uncertainty_score":0.01262766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2348475168951484,"score_gpt":0.4032111303280295,"score_spread":0.1683636134328811,"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."}}