{"id":"W4399602478","doi":"10.2196/55632","title":"It Is in Our DNA: Bringing Electronic Health Records and Genomic Data Together for Precision Medicine","year":2024,"lang":"en","type":"article","venue":"JMIR Bioinformatics and Biotechnology","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Precision medicine; Workflow; Data science; Point of care; Clinical decision support system; Genomic medicine; Computer science; Genomic information; Health care; Personalized medicine; Digital health; Medical record; Big data; Data mining; Medicine; Bioinformatics; Decision support system; Computational biology; Genome; Biology; Pathology; Database","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":[0.0003444435,0.0001492608,0.000194773,0.0001755685,0.00006665669,0.00003716981,0.0002254316,0.0002471394,0.00000341316],"category_scores_gemma":[0.00003483147,0.0001189712,0.00002545609,0.0001047406,0.00006462651,0.00001001289,0.0003767935,0.0001279672,0.000002075255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002412454,"about_ca_system_score_gemma":0.0001207367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002502312,"about_ca_topic_score_gemma":0.0001314585,"domain_scores_codex":[0.9989662,0.000009181085,0.0003240583,0.0003239358,0.00004934814,0.0003273245],"domain_scores_gemma":[0.999464,0.0000121489,0.00006816322,0.0003823036,0.00001533919,0.00005808209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002466817,0.0001059208,0.000742842,0.001797222,0.0002591566,0.00001061112,0.001743874,0.000005825661,0.08884311,0.002563323,0.1381361,0.7655454],"study_design_scores_gemma":[0.001137099,0.001493484,0.0005250907,0.0002752127,0.00002974144,0.0001846178,0.001913142,0.03672945,0.003899272,0.001358921,0.9520479,0.0004060175],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.906967,0.02674789,0.005937513,0.05808897,0.000292175,0.001270013,0.000412368,0.00006853389,0.0002155284],"genre_scores_gemma":[0.9740966,0.01968498,0.002882419,0.002447728,0.0001485566,0.00003878302,0.0004327418,0.00002951649,0.0002386685],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8139119,"threshold_uncertainty_score":0.4851504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01543198435434441,"score_gpt":0.3091771541557483,"score_spread":0.2937451698014039,"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."}}