{"id":"W4388521771","doi":"10.1609/aaaiss.v1i1.27491","title":"XGBoost for Interpretable Alzheimer’s Decision Support","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Symposium Series","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; McMaster University; Population Health Research Institute","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; Natural Sciences and Engineering Research Council of Canada; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Ministère de la Défense Nationale; Alzheimer's Association","keywords":"Interpretability; Recall; Artificial intelligence; Disease; Machine learning; Computer science; Clinical decision support system; Decision support system; Cognition; Process (computing); Medicine; Data science; Psychology; Cognitive psychology; Pathology; Psychiatry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007131991,0.0001788741,0.0002518647,0.0001473472,0.0003009329,0.0001833758,0.00200216,0.00008365275,0.00001278939],"category_scores_gemma":[0.0003769257,0.0001324285,0.0001488864,0.0007825713,0.00008583938,0.0009471467,0.001124803,0.0001749933,0.00004676966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002920688,"about_ca_system_score_gemma":0.0000819182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003047834,"about_ca_topic_score_gemma":0.0000040654,"domain_scores_codex":[0.998385,0.000009332425,0.0003852558,0.0004142068,0.0003865324,0.0004196792],"domain_scores_gemma":[0.9987074,0.0001911757,0.000266168,0.0003445516,0.0004102678,0.00008042626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006141739,0.0001520479,0.07631102,0.002064257,0.0003218433,0.000003789373,0.02616451,0.0005976831,0.1235003,0.4856731,0.2434222,0.04117513],"study_design_scores_gemma":[0.00191923,0.002962632,0.02342385,0.001310522,0.0002357798,0.0002823624,0.001351009,0.1235928,0.367081,0.1236235,0.3524333,0.001783879],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6827552,0.0008581092,0.01826071,0.2056613,0.01335585,0.007304454,0.0001320643,0.005497775,0.06617455],"genre_scores_gemma":[0.9593939,0.00007012934,0.03560939,0.0005119643,0.0001593879,0.0001830824,0.00000400845,0.00004780354,0.004020308],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3620495,"threshold_uncertainty_score":0.5400278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0205458961332051,"score_gpt":0.2854560813562283,"score_spread":0.2649101852230232,"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."}}