{"id":"W3164924750","doi":"10.2196/28218","title":"Head and Tail Entity Fusion Model in Medical Knowledge Graph Construction: Case Study for Pituitary Adenoma","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Chinese Academy of Medical Sciences Initiative for Innovative Medicine; Peking Union Medical College; Chinese Academy of Medical Sciences","keywords":"Computer science; Pituitary adenoma; Artificial intelligence; Graph; Economic shortage; Machine learning; Information retrieval; Natural language processing; Adenoma; Medicine; Pathology","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":[0.0005803996,0.0001898036,0.0003298629,0.0001592126,0.0001853974,0.00007405078,0.0004633826,0.0002481687,0.00002775009],"category_scores_gemma":[0.0002154231,0.0001680464,0.00007061992,0.0007066512,0.0002462139,0.000797625,0.000737244,0.0005824327,0.000004137623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003760568,"about_ca_system_score_gemma":0.0003886338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000974876,"about_ca_topic_score_gemma":0.0005545022,"domain_scores_codex":[0.9977044,0.00008283315,0.0007954334,0.0002512273,0.0007885715,0.0003775442],"domain_scores_gemma":[0.9984767,0.0003109568,0.0001174937,0.0004194341,0.0001475727,0.0005277863],"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":[0.00006500895,0.002410928,0.01005325,0.0007674139,0.00009344781,0.01054994,0.03950406,0.0007716658,0.000006517572,0.03140163,0.004849214,0.8995269],"study_design_scores_gemma":[0.002449626,0.0002009047,0.0002956457,0.0001434668,0.000008939807,0.006255581,0.00400193,0.9798772,0.00001060617,0.005964329,0.0005534966,0.000238297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6170879,0.0002516717,0.3808293,0.0006178449,0.0003963073,0.000517316,0.000003697688,0.00009081998,0.0002051432],"genre_scores_gemma":[0.9306678,0.0001617345,0.06770487,0.001123758,0.0001264243,0.0001597653,0.00001089551,0.00001163943,0.00003308623],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9791055,"threshold_uncertainty_score":0.6852732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02464694647546247,"score_gpt":0.3306699936568903,"score_spread":0.3060230471814278,"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."}}