{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008057699,0.0006248507,0.0009675532,0.0008370202,0.001275955,0.004275363,0.001944978,0.002042451,0.004623863],"category_scores_gemma":[0.01791975,0.00042231,0.001213923,0.001372109,0.002493798,0.008238583,0.004258641,0.004299753,0.002091018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00170653,"about_ca_system_score_gemma":0.002943976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001252649,"about_ca_topic_score_gemma":0.000852187,"domain_scores_codex":[0.9917762,0.00388073,0.0005929809,0.001019997,0.002067949,0.0006620346],"domain_scores_gemma":[0.9889107,0.004707714,0.0006542658,0.004699057,0.0008214063,0.0002068758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002874107,0.0001483934,0.00220437,0.000357805,0.00009316462,0.0004007888,0.0004123239,0.06715301,0.004152363,0.73768,0.01940775,0.1677026],"study_design_scores_gemma":[0.00009507404,0.0001199669,0.0005275838,0.0001507843,0.00004061293,0.0005925915,0.0001123004,0.3331285,0.008778937,0.6153143,0.04108649,0.00005268193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01040172,0.001631722,0.9694729,0.007555452,0.0003262495,0.0001731385,0.0005883395,0.001146359,0.008704058],"genre_scores_gemma":[0.5874408,0.00325909,0.3945965,0.003404069,0.0007537089,0.0005052085,0.001242004,0.0003026164,0.008495988],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008057699,"threshold_uncertainty_score":0.04261369,"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."}}