{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001819773,0.0007775541,0.0006457585,0.00192738,0.0003069651,0.001303582,0.0009556236,0.0006544081,0.004802962],"category_scores_gemma":[0.007832079,0.0003366431,0.0007229256,0.001212403,0.0002872109,0.001237135,0.001971096,0.0009098415,0.00126452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008250846,"about_ca_system_score_gemma":0.00103384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003096959,"about_ca_topic_score_gemma":0.004795679,"domain_scores_codex":[0.9988325,0.0003679803,0.0001199691,0.0002957507,0.0003250953,0.00005865144],"domain_scores_gemma":[0.9980793,0.0009435756,0.0002673146,0.0003241885,0.0002970076,0.0000886233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001267927,0.0003839429,0.04097158,0.0004638608,0.0003893648,0.0005542387,0.0004629508,0.1122049,0.006358552,0.01534034,0.04201284,0.7795894],"study_design_scores_gemma":[0.0001350725,0.0001505489,0.007637228,0.00005973474,0.00005284261,0.0004478495,0.00009860538,0.9330947,0.007624096,0.03106418,0.01959758,0.00003746659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05452834,0.000435261,0.9004755,0.0009157132,0.0001256871,0.0005762688,0.01628512,0.02303302,0.003625159],"genre_scores_gemma":[0.404044,0.0003822387,0.5616444,0.0003954626,0.00009014147,0.0007232273,0.02865784,0.0007895793,0.003273125],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004802962,"threshold_uncertainty_score":0.01606756,"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."}}