{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05381672,0.0008423264,0.00153615,0.006732791,0.002934402,0.0190127,0.002954384,0.006718746,0.007874263],"category_scores_gemma":[0.1079882,0.001070893,0.001976131,0.008603785,0.009328234,0.03292377,0.0121411,0.01277502,0.004839033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004127577,"about_ca_system_score_gemma":0.01275769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004790898,"about_ca_topic_score_gemma":0.004999112,"domain_scores_codex":[0.9537924,0.02654866,0.003800693,0.002966193,0.01151773,0.0013744],"domain_scores_gemma":[0.8799036,0.07358722,0.008533337,0.01676586,0.01528028,0.005929724],"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":[0.0002047003,0.000119193,0.01046278,0.002010116,0.0003603239,0.0007205368,0.005339968,0.0007221875,0.002330979,0.1450176,0.1525138,0.6801979],"study_design_scores_gemma":[0.0000420107,0.0001212614,0.003458327,0.00559403,0.0002025174,0.001255797,0.003505105,0.0007542965,0.001500735,0.2352235,0.7481595,0.000183008],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.005519406,0.07784259,0.1041114,0.7764602,0.01177318,0.0002014358,0.001103187,0.001561516,0.02142707],"genre_scores_gemma":[0.09987619,0.1366699,0.4377642,0.2818332,0.02950423,0.0003295082,0.002553195,0.00117049,0.01029909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05381672,"threshold_uncertainty_score":0.2846134,"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."}}