{"id":"W4390097263","doi":"10.1109/jbhi.2023.3346210","title":"EHR-HGCN: An Enhanced Hybrid Approach for Text Classification Using Heterogeneous Graph Convolutional Networks in Electronic Health Records","year":2023,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Topic Modeling","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China; Natural Science Foundation of Jilin Province","keywords":"Computer science; Sentence; Artificial intelligence; Natural language processing; Graph; Convolutional neural network; Graph database; Text graph; Biomedical text mining; Text mining; Information retrieval; Theoretical computer science","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":[0.0007354459,0.001038528,0.0005292804,0.001999995,0.0004754741,0.00063348,0.001414214,0.0009866829,0.001730549],"category_scores_gemma":[0.002175422,0.0002759705,0.000802446,0.001604095,0.0003734868,0.001966027,0.001018274,0.001128609,0.0007743185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001107315,"about_ca_system_score_gemma":0.001255693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01937381,"about_ca_topic_score_gemma":0.03164958,"domain_scores_codex":[0.9994511,0.0001156399,0.00003527115,0.0001888229,0.0001270131,0.00008219314],"domain_scores_gemma":[0.9993188,0.0002397625,0.00007884631,0.0001301906,0.0001895607,0.00004284217],"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":[0.0004311431,0.0004674509,0.007062699,0.0002275523,0.000265588,0.0003767617,0.0002597606,0.1712984,0.01533686,0.008224493,0.02353351,0.7725158],"study_design_scores_gemma":[0.00001536998,0.00005000699,0.00116297,0.00001343545,0.00004344302,0.00004662209,0.00003721538,0.9851401,0.004211533,0.006141236,0.003125247,0.00001287107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1477386,0.001808121,0.8286915,0.001475138,0.0002894622,0.0002965522,0.002715152,0.01235191,0.004633531],"genre_scores_gemma":[0.596673,0.0008294386,0.374269,0.0009847496,0.0002504981,0.0002571369,0.01086339,0.0004730388,0.01539975],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01937381,"threshold_uncertainty_score":0.03852206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08133374493038933,"score_gpt":0.3412262027901213,"score_spread":0.259892457859732,"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."}}