{"id":"W2950942463","doi":"10.15252/msb.20188497","title":"netDx: interpretable patient classification using integrated patient similarity networks","year":2019,"lang":"en","type":"article","venue":"Molecular Systems Biology","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":106,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; University of Toronto; Mount Sinai Hospital; Centre for Addiction and Mental Health","funders":"National Institute of General Medical Sciences; National Human Genome Research Institute; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Biology; Similarity (geometry); Computational biology; Patient care; Artificial intelligence; Bioinformatics; Computer science","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":[0.0003394515,0.0002301861,0.0003532809,0.0001362176,0.00009584807,0.0001127913,0.0006171674,0.0002898407,0.00001641301],"category_scores_gemma":[0.00008322194,0.0002023375,0.0000828875,0.000382678,0.00003784435,0.0001228701,0.0003319984,0.0004438245,0.00006244169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002221013,"about_ca_system_score_gemma":0.0001061008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001425167,"about_ca_topic_score_gemma":0.000008298229,"domain_scores_codex":[0.996997,0.001117237,0.0005516013,0.000687314,0.0001690443,0.0004777897],"domain_scores_gemma":[0.9982363,0.00007649689,0.00035228,0.000989585,0.0002284329,0.0001169054],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008136247,0.0002661087,0.165747,0.0003997482,0.0002324096,0.0001103265,0.002580094,0.4887806,0.08574222,0.141686,0.0005797235,0.1137944],"study_design_scores_gemma":[0.000125183,0.0003518445,0.0006427853,0.00008705112,0.000004701774,0.00004264196,0.00006860495,0.9910216,0.0002312185,0.00006413396,0.007141246,0.0002189889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3936583,0.0007505387,0.602166,0.0001453459,0.002066109,0.000547612,0.000003520844,0.0001450253,0.000517554],"genre_scores_gemma":[0.9950611,0.000004412008,0.004278376,0.0005003135,0.00003299124,0.00003782445,0.00004334232,0.00002010831,0.00002153074],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6014028,"threshold_uncertainty_score":0.8251083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01492347302979893,"score_gpt":0.26516715735558,"score_spread":0.250243684325781,"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."}}