{"id":"W4206666681","doi":"10.2196/31918","title":"The Development History and Research Tendency of Medical Informatics: Topic Evolution Analysis","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Health informatics; Informatics; Data science; Computer science; Latent Dirichlet allocation; Medical research; Usability; Public health informatics; Translational bioinformatics; Topic model; Information retrieval; Medicine; Engineering; Public health; 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":[0.005575044,0.0001090867,0.000262521,0.000290935,0.0002760883,0.00007479802,0.001285626,0.0002539063,0.000193626],"category_scores_gemma":[0.002012473,0.00007528627,0.00006347022,0.00107924,0.0003368327,0.0003456698,0.001147013,0.001038125,0.00002735163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003524135,"about_ca_system_score_gemma":0.003252378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005390454,"about_ca_topic_score_gemma":0.0001777867,"domain_scores_codex":[0.9936085,0.0003088302,0.001271283,0.0001033897,0.004307029,0.0004009384],"domain_scores_gemma":[0.9973087,0.0008660989,0.0002479055,0.0006340445,0.0005078745,0.0004353837],"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":[0.00000849414,0.0001491455,0.02628823,0.001790127,0.0003541219,0.0000340376,0.1420049,0.00004002191,0.000001231946,0.2049363,0.0104851,0.6139083],"study_design_scores_gemma":[0.0004281901,0.00007952842,0.02730502,0.0002330256,0.00002046023,0.00005958725,0.006877686,0.816318,0.00002444423,0.001029021,0.1474305,0.0001945391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3105401,0.003888367,0.6460719,0.01759161,0.001240425,0.0007080538,0.000002107779,0.0002668323,0.01969061],"genre_scores_gemma":[0.8947718,0.0008403395,0.1010522,0.001742339,0.0001929425,0.0001348804,0.00002969982,0.00001380447,0.001221987],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.816278,"threshold_uncertainty_score":0.5769578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04272650836466017,"score_gpt":0.3753731085118522,"score_spread":0.3326466001471921,"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."}}