{"id":"W3027889410","doi":"10.1038/s41467-020-16378-3","title":"Inferring multimodal latent topics from electronic health records","year":2020,"lang":"en","type":"article","venue":"Nature Communications","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Canada First Research Excellence Fund; Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Government of Canada; J. Willard and Alice S. Marriott Foundation; Natural Sciences and Engineering Research Council of Canada; Mayo Clinic","keywords":"Health records; Leverage (statistics); Computer science; Electronic health record; Informatics; Diagnosis code; Data science; Health informatics; Machine learning; Data mining; Artificial intelligence; Medicine; Health care; Pathology; Public health","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":[0.004334602,0.001067416,0.0009974218,0.002419185,0.0004892955,0.001502723,0.001387172,0.001450716,0.001526459],"category_scores_gemma":[0.01113996,0.000622165,0.001898092,0.001994319,0.0005640306,0.001771153,0.00156118,0.001770037,0.001104361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008287099,"about_ca_system_score_gemma":0.001218269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009692028,"about_ca_topic_score_gemma":0.01322757,"domain_scores_codex":[0.9979108,0.0009724388,0.0001023789,0.0006207314,0.0002013581,0.0001921748],"domain_scores_gemma":[0.9925132,0.005954723,0.0004448809,0.0006522833,0.0002884242,0.0001465609],"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":[0.002018183,0.001140363,0.12725,0.000662934,0.001035712,0.001068585,0.002366115,0.3583886,0.01043203,0.03663679,0.03208565,0.426915],"study_design_scores_gemma":[0.00006133857,0.00006027627,0.008000158,0.00004931495,0.00009698885,0.0001391995,0.0001200667,0.9679685,0.0008952308,0.01989702,0.002681058,0.00003092332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3088284,0.003149636,0.6688268,0.003240805,0.0001461613,0.0002810514,0.01036678,0.002812528,0.002347977],"genre_scores_gemma":[0.8695939,0.001467042,0.1040487,0.0005780848,0.0005581768,0.0002439269,0.02039935,0.0001485424,0.002962369],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009692028,"threshold_uncertainty_score":0.02292383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03782611631966606,"score_gpt":0.3467720537823082,"score_spread":0.3089459374626422,"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."}}