{"id":"W3158396170","doi":"10.1007/978-3-031-45673-2_38","title":"IA-GCN: Interpretable Attention Based Graph Convolutional Network for Disease Prediction","year":2023,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Institute on Aging; National Institutes of Health; Technische Universität München","keywords":"Interpretability; Computer science; Novelty; Artificial intelligence; Machine learning; Graph; Task (project management); Domain (mathematical analysis); Feature (linguistics); Medical diagnosis; Theoretical computer science; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001637671,0.0002001627,0.000187448,0.0005077111,0.0005429034,0.0003053453,0.001383617,0.00007768671,0.000006227966],"category_scores_gemma":[0.0003570338,0.0001931666,0.0001076458,0.003732392,0.0002244003,0.000631867,0.0004139084,0.0003018157,0.00002140629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001727782,"about_ca_system_score_gemma":0.0004641851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004257302,"about_ca_topic_score_gemma":0.00003199888,"domain_scores_codex":[0.9970919,0.0001423607,0.0003466046,0.0009873671,0.0006476889,0.0007840347],"domain_scores_gemma":[0.9979602,0.0007461761,0.0001225217,0.0007057356,0.0002365556,0.0002288706],"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":[0.00001922777,0.00002580126,0.06546776,0.00006414542,0.000002948659,0.000007483644,0.0001185791,0.8610008,0.00007787525,0.001847842,0.0002794692,0.07108808],"study_design_scores_gemma":[0.0002754475,0.0001054206,0.1252908,0.0001206074,0.000002756949,0.000003431041,1.699946e-7,0.8441831,0.00003569253,0.02964316,0.0001836864,0.0001557067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01059224,0.00008487,0.9798955,0.005105201,0.003219306,0.0005190277,0.00001534225,0.000561957,0.00000657408],"genre_scores_gemma":[0.7490864,0.000002797308,0.2491189,0.001272075,0.0003895122,0.00008318778,0.00003315815,0.00001039055,0.000003653933],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7384941,"threshold_uncertainty_score":0.7877107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01484199793717927,"score_gpt":0.2892752715201007,"score_spread":0.2744332735829215,"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."}}