{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004514569,0.001189706,0.0007824741,0.0009394711,0.0003740375,0.000527412,0.00177227,0.00142623,0.003829781],"category_scores_gemma":[0.001279087,0.000359076,0.0008061351,0.0008408513,0.0003155422,0.000714657,0.001108835,0.001601172,0.001271289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001149855,"about_ca_system_score_gemma":0.001319239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0402353,"about_ca_topic_score_gemma":0.05922453,"domain_scores_codex":[0.9998152,0.00002706456,0.000007013231,0.00007917473,0.00002885413,0.00004271303],"domain_scores_gemma":[0.999709,0.000105542,0.00002259608,0.00005180024,0.00007439336,0.00003668062],"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":[0.0006585319,0.0004817557,0.006614518,0.0002782798,0.0003568096,0.0004502146,0.0001070152,0.2466931,0.02262922,0.008668416,0.05640518,0.6566569],"study_design_scores_gemma":[0.00002144891,0.00005401587,0.0008943317,0.00001773533,0.00004018941,0.00004794787,0.000009659542,0.9869362,0.003020194,0.006993114,0.001952807,0.0000124246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1263221,0.005211765,0.8151357,0.002170503,0.001057508,0.0003528787,0.01081103,0.03010879,0.008829773],"genre_scores_gemma":[0.746443,0.001477053,0.2182921,0.001520201,0.0002642599,0.0002772741,0.01318257,0.0005622981,0.01798114],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0402353,"threshold_uncertainty_score":0.08000225,"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."}}