{"id":"W3195146091","doi":"10.1016/j.patrec.2021.07.004","title":"Machine Learning in Precision Medicine to Preserve Privacy via Encryption","year":2021,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research and Productivity Council; University of Victoria","funders":"National Research Council Canada; National Research Council","keywords":"Precision medicine; Computer science; Encryption; Machine learning; Field (mathematics); Cloud computing; Personalized medicine; Artificial intelligence; Data science; Health care; Data mining; Computer security; Bioinformatics; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001158077,0.00009350428,0.0001068102,0.00006438753,0.00004653966,0.000008286243,0.0001519833,0.00007850421,0.00007902479],"category_scores_gemma":[0.0002908358,0.0001057586,0.00003927332,0.0002714118,0.00002474284,0.000005222993,0.0002537095,0.00009369168,0.00001819343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004514108,"about_ca_system_score_gemma":0.00005591915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002279387,"about_ca_topic_score_gemma":0.0004870243,"domain_scores_codex":[0.9992687,0.00005635042,0.0001046807,0.000378244,0.00003700353,0.0001550977],"domain_scores_gemma":[0.9994792,0.00003299307,0.00003956204,0.0002728746,0.00008181754,0.00009350807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006924611,0.0002646931,0.187768,0.00004622414,0.0000743445,0.0004567369,0.0004176892,0.2179246,0.5756443,0.004961184,0.001943195,0.009806609],"study_design_scores_gemma":[0.01026961,0.002795882,0.1860841,0.0003477928,0.0001850572,0.0000680748,0.0007566297,0.1171656,0.2181786,0.01323098,0.4490964,0.001821261],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9685207,0.0003924438,0.02918548,0.0002186503,0.0001044213,0.00010313,0.000005246083,0.000009564167,0.00146034],"genre_scores_gemma":[0.9966669,0.0009089903,0.0001814711,0.0001772834,0.00009781079,5.756424e-7,0.00009446454,0.00001085414,0.00186163],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4471532,"threshold_uncertainty_score":0.431271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03168615405114084,"score_gpt":0.198564469855497,"score_spread":0.1668783158043561,"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."}}