{"id":"W4380052386","doi":"10.3390/bioengineering10060701","title":"Personalized Explanations for Early Diagnosis of Alzheimer’s Disease Using Explainable Graph Neural Networks with Population Graphs","year":2023,"lang":"en","type":"article","venue":"Bioengineering","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; H. Lundbeck A/S; Servier; Eisai; Meso Scale Diagnostics; National Research Foundation of Korea; National Research Foundation; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Pfizer; BioClinica; Biogen; Ministry of Science and ICT, South Korea; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Interpretability; Neuroimaging; Graph; Computer science; Correlation; Artificial intelligence; Machine learning; Population; Disease; Data science; Psychology; Medicine; Theoretical computer science; Neuroscience; Mathematics; Pathology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001129646,0.0008767557,0.0005328204,0.001253112,0.0003729105,0.0008265179,0.0009298287,0.001095225,0.001797609],"category_scores_gemma":[0.007787106,0.0004063129,0.0008297446,0.0006479881,0.0004740436,0.001336914,0.0008404148,0.001444898,0.0002765444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001224071,"about_ca_system_score_gemma":0.0009748376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0118821,"about_ca_topic_score_gemma":0.01756049,"domain_scores_codex":[0.9995797,0.0001863088,0.00001572221,0.0001342795,0.00004546289,0.00003838515],"domain_scores_gemma":[0.997184,0.002043063,0.0003197141,0.0001587564,0.0002022164,0.00009222091],"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":[0.0001803862,0.000112226,0.01848002,0.0001217652,0.0002029974,0.0002804963,0.0002639986,0.8613951,0.00127169,0.02140689,0.003301298,0.09298322],"study_design_scores_gemma":[0.00000787382,0.00001618897,0.0008076449,0.00001155313,0.00002261726,0.00002853511,0.00001280507,0.9781936,0.000180268,0.02030879,0.0004028318,0.000007247971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1278411,0.001047502,0.8649122,0.002465451,0.0001049554,0.00007733253,0.0008129448,0.0008777062,0.001860904],"genre_scores_gemma":[0.9236456,0.000502303,0.07307997,0.0003045527,0.00009433314,0.00007732058,0.0007684071,0.00006935818,0.001458111],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0118821,"threshold_uncertainty_score":0.02362585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04208861863036303,"score_gpt":0.2901991524450853,"score_spread":0.2481105338147222,"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."}}