{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001647177,0.00013428,0.0001579132,0.0003492038,0.0001597413,0.00005465592,0.0002346511,0.00003757705,0.000002089023],"category_scores_gemma":[0.00005360808,0.0001307287,0.00008084634,0.001034087,0.00001714808,0.0003118622,0.00005463866,0.00008897825,4.137808e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002335491,"about_ca_system_score_gemma":0.00001878031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000352641,"about_ca_topic_score_gemma":0.000007899853,"domain_scores_codex":[0.9989873,0.00003179741,0.0002036326,0.0002615028,0.000200553,0.0003151865],"domain_scores_gemma":[0.9992992,0.0001347247,0.00009079344,0.0002659707,0.00008292141,0.0001264195],"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":[0.00001188858,0.000009401434,0.1496033,0.00008207764,0.00002646189,0.000006718541,0.0002642008,0.8410355,0.00001164665,0.007558794,0.00003259153,0.001357389],"study_design_scores_gemma":[0.0002044635,0.00004868806,0.1429795,0.00005753644,0.00002291986,0.000001611601,0.00001994222,0.8563631,0.00001452113,0.0001185363,0.00003926538,0.0001299416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5193047,0.0004843831,0.478895,0.0003010642,0.0002482928,0.0003843754,0.00001152354,0.0003686521,0.000001993675],"genre_scores_gemma":[0.9759777,0.00001690244,0.0236938,0.00001877516,0.00004793624,0.0001734853,0.00004119417,0.00002464803,0.000005593526],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.456673,"threshold_uncertainty_score":0.5330963,"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."}}