{"id":"W2919720720","doi":"10.3389/fmed.2019.00034","title":"From Big Data to Precision Medicine","year":2019,"lang":"en","type":"review","venue":"Frontiers in Medicine","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":464,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Wellcome Trust","keywords":"Big data; Data science; Standardization; Variety (cybernetics); Computer science; Data sharing; Analytics; Population; Meaning (existential); Data mining; Medicine; Artificial intelligence","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":[0.02500672,0.00154686,0.002862836,0.007787095,0.002430565,0.01473759,0.003879679,0.006153674,0.01501534],"category_scores_gemma":[0.07077319,0.0009736221,0.001437041,0.008249074,0.01390694,0.02050196,0.0119573,0.01217213,0.005379156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005968148,"about_ca_system_score_gemma":0.007658619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003941474,"about_ca_topic_score_gemma":0.002446181,"domain_scores_codex":[0.9772423,0.0111466,0.001065519,0.002470873,0.00730528,0.0007695292],"domain_scores_gemma":[0.9387542,0.04340018,0.002141531,0.007441249,0.006091277,0.002171661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001565014,0.0000417783,0.001864542,0.002363265,0.000258031,0.0001903642,0.0007473672,0.002164198,0.0002701228,0.535668,0.2406892,0.2155866],"study_design_scores_gemma":[0.00003009164,0.00003981358,0.0006741324,0.001804223,0.00004595294,0.0001371497,0.0004010702,0.001640021,0.0002635532,0.6664101,0.3284931,0.00006085337],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"review","genre_scores_codex":[0.002528509,0.39414,0.118069,0.4028564,0.01971191,0.0002218736,0.004352274,0.001623743,0.05649623],"genre_scores_gemma":[0.1449164,0.4645782,0.161008,0.1364409,0.06849949,0.0009857274,0.006142004,0.001136441,0.01629297],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.02500672,"threshold_uncertainty_score":0.1322498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2161396992265188,"score_gpt":0.4305946613213262,"score_spread":0.2144549620948073,"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."}}