{"id":"W3126209695","doi":"10.2196/23587","title":"Novel Graph-Based Model With Biaffine Attention for Family History Extraction From Clinical Text: Modeling Study","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Harbin Institute of Technology","keywords":"Computer science; Relationship extraction; Family history; Information extraction; Natural language processing; Sentence; Information retrieval; Artificial intelligence; Genogram; ENCODE; Graph; Theoretical computer science; Psychology; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009040158,0.0001952423,0.0003445131,0.0001178134,0.0001016706,0.00008249748,0.0005886446,0.0002188213,0.00001312007],"category_scores_gemma":[0.0001713098,0.0001664984,0.0001451649,0.000202454,0.00005301302,0.0007743168,0.0001531232,0.0004705461,0.000009208967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001465466,"about_ca_system_score_gemma":0.000966604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003544934,"about_ca_topic_score_gemma":0.00006519842,"domain_scores_codex":[0.9970747,0.00004878486,0.001169254,0.0003189663,0.001090327,0.0002979545],"domain_scores_gemma":[0.9981469,0.0002529133,0.0002751415,0.0007212033,0.0003309367,0.0002729302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002416927,0.005969068,0.002043857,0.0003446477,0.0003103646,0.00005891569,0.008295647,0.80791,0.0005356829,0.00597506,0.003838082,0.164477],"study_design_scores_gemma":[0.002614643,0.000163987,0.0002833611,0.0000990999,0.00003182581,0.00000596794,0.0006510919,0.9951848,0.000005959585,0.0003209331,0.0004260057,0.0002122825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2467306,0.00003859668,0.7517954,0.0003064801,0.0004393487,0.0003936796,0.000005119046,0.0001496971,0.000141098],"genre_scores_gemma":[0.5031151,0.000008087609,0.4947134,0.001765864,0.0001548375,0.0001201877,0.00004422418,0.0000151904,0.00006319682],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.257082,"threshold_uncertainty_score":0.6789606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09582043481915976,"score_gpt":0.34475927771473,"score_spread":0.2489388428955703,"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."}}