{"id":"W3088981549","doi":"10.2196/18287","title":"Construction of a Digestive System Tumor Knowledge Graph Based on Chinese Electronic Medical Records: Development and Usability Study","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Schema (genetic algorithms); Readability; Knowledge extraction; Graph; Information retrieval; Usability; Knowledge graph; Natural language processing; Data mining; Artificial intelligence; Theoretical computer science","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":[0.0006574245,0.0001760548,0.0003244165,0.0000464665,0.00006411097,0.000009673027,0.0002312595,0.0002401097,0.00002729384],"category_scores_gemma":[0.001310246,0.0001232913,0.00005297785,0.0002008573,0.0004407666,0.000004296547,0.0001401004,0.0003163708,0.000005819547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003069367,"about_ca_system_score_gemma":0.000718934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003739505,"about_ca_topic_score_gemma":0.00001747967,"domain_scores_codex":[0.9982134,0.0001079168,0.0006541552,0.0001833502,0.0005975346,0.000243721],"domain_scores_gemma":[0.9990726,0.000109743,0.0001648579,0.0001753586,0.0000769393,0.0004005348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001992841,0.003192249,0.3249188,0.004442724,0.0006180723,0.00006125185,0.02341821,0.00001468488,0.0002374473,0.0005642871,0.002390004,0.6381494],"study_design_scores_gemma":[0.04305934,0.0519197,0.327248,0.004279455,0.0003884401,0.0007792413,0.1563992,0.2827086,0.01421721,0.0002309058,0.1141816,0.004588239],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952447,0.00009951695,0.003327379,0.0002609761,0.00009834172,0.0003364171,0.000005844662,0.00004473501,0.0005820269],"genre_scores_gemma":[0.9975228,0.00001167441,0.001866937,0.0004135701,0.00008890289,0.0000581171,0.00002692307,0.000007454277,0.000003586494],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6335612,"threshold_uncertainty_score":0.5027675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01102230976607863,"score_gpt":0.2829604987264354,"score_spread":0.2719381889603567,"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."}}