{"id":"W2942356725","doi":"10.3233/978-1-61499-959-1-189","title":"Discovering Monogenic Causes of Multi-Diseases by Mining Electronic Medical Records and Genetics Repositories","year":2019,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Disease; Medical genetics; Gene; Genome; Computational biology; Bioinformatics; Genetics; Data science; Biology; Medicine; Computer science; Pathology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.004871965,0.001176161,0.001116707,0.02752421,0.001251373,0.002549085,0.001899804,0.001410956,0.00232825],"category_scores_gemma":[0.01712279,0.0005505206,0.002571027,0.01608483,0.0006412358,0.002406474,0.002524651,0.0009327884,0.0008618688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009132344,"about_ca_system_score_gemma":0.00431411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004442593,"about_ca_topic_score_gemma":0.008145782,"domain_scores_codex":[0.994545,0.0009567952,0.001044321,0.001978927,0.001249815,0.0002252048],"domain_scores_gemma":[0.9787732,0.01164217,0.004641427,0.002587544,0.001576792,0.0007787616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008770564,0.0007163201,0.5778847,0.004501982,0.003190853,0.01329947,0.001555911,0.007442027,0.01576739,0.0152933,0.01661952,0.3428515],"study_design_scores_gemma":[0.0005013127,0.0008454575,0.5430809,0.002232978,0.007408611,0.02889005,0.00369455,0.1449094,0.03522272,0.1072577,0.1254097,0.000546592],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4770428,0.02082464,0.3755125,0.004784302,0.000554478,0.001582168,0.1025585,0.01008127,0.007059458],"genre_scores_gemma":[0.5433817,0.005129373,0.3657928,0.0008306768,0.0004752197,0.0005886115,0.0816467,0.0003419527,0.001813046],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02752421,"threshold_uncertainty_score":0.02576572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00976752641689149,"score_gpt":0.3009489724865824,"score_spread":0.2911814460696909,"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."}}