{"id":"W4307385604","doi":"10.1038/s41598-022-22956-w","title":"Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research","keywords":"Computer science; End-to-end principle; Graph; Data science; Knowledge graph; End user; Data mining; Information retrieval; World Wide Web; Theoretical computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0020446,0.00105847,0.0009508355,0.001950984,0.0004923278,0.001203543,0.002119236,0.001892611,0.002117015],"category_scores_gemma":[0.008024601,0.000609598,0.001967898,0.002323511,0.0005203545,0.002252563,0.001538628,0.002478804,0.001373601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001240935,"about_ca_system_score_gemma":0.001556383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01846914,"about_ca_topic_score_gemma":0.03178914,"domain_scores_codex":[0.9990397,0.0003279544,0.00006136933,0.0003700971,0.0001106165,0.00009026378],"domain_scores_gemma":[0.9966925,0.002347354,0.0002059351,0.000275959,0.0003767726,0.000101474],"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":[0.000409997,0.0003811631,0.01302855,0.0003521432,0.0003254122,0.0003005057,0.0006441467,0.6876897,0.002701521,0.0210249,0.01451734,0.2586246],"study_design_scores_gemma":[0.00001723409,0.00002483369,0.0005395265,0.00001281158,0.00002939241,0.00003195089,0.00002334247,0.9832751,0.0003605269,0.01452196,0.001154772,0.000008642218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03547241,0.0008101951,0.9532983,0.001210227,0.00007224063,0.0001974453,0.004515109,0.003320079,0.001104031],"genre_scores_gemma":[0.5306199,0.001213514,0.4358032,0.000839866,0.0002790417,0.0007939583,0.02417045,0.0004242662,0.005855741],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01846914,"threshold_uncertainty_score":0.03672332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1018581186794975,"score_gpt":0.3765843468933598,"score_spread":0.2747262282138623,"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."}}