{"id":"W4224212599","doi":"10.1101/2022.04.10.22273666","title":"Multiple Cost Optimisation for Alzheimer’s Disease Diagnosis","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Interreg; National Institute on Aging; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Ulster University; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; European Commission; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Computer science; Weighting; Feature selection; Feature (linguistics); Budget constraint; Selection (genetic algorithm); Hyperparameter; Machine learning; Cost estimate; Dementia; Risk analysis (engineering); Artificial intelligence; Operations research; Engineering; Business; Economics","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":[0.005265958,0.001182879,0.00122588,0.00124129,0.0003059125,0.001413545,0.0009676339,0.001228297,0.005199727],"category_scores_gemma":[0.01765251,0.0006116635,0.001132813,0.0008310811,0.0006308528,0.001273935,0.001013272,0.001547222,0.0004302168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00214917,"about_ca_system_score_gemma":0.001584435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005617604,"about_ca_topic_score_gemma":0.003981871,"domain_scores_codex":[0.9972085,0.001894884,0.0001005541,0.0002503937,0.0003901813,0.00015541],"domain_scores_gemma":[0.9938383,0.005121669,0.0003227484,0.0001585803,0.0004104466,0.0001483449],"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":[0.0002605581,0.0001044355,0.001300593,0.0002432925,0.0001405389,0.000081226,0.00003894509,0.9201323,0.0006125688,0.007306392,0.002828934,0.06695027],"study_design_scores_gemma":[0.00005326707,0.00007775127,0.0006182442,0.00006266508,0.0000430071,0.00004379858,0.00001701771,0.9856976,0.0004492232,0.01151224,0.001411292,0.00001395272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08535689,0.00449199,0.8945522,0.003536804,0.0001932113,0.0004286816,0.0005211751,0.0007415634,0.01017754],"genre_scores_gemma":[0.7893518,0.0009171142,0.2031834,0.0005301592,0.00008696541,0.0004190202,0.0004195284,0.0002104161,0.004881497],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005617604,"threshold_uncertainty_score":0.02784932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5144329879916312,"score_gpt":0.453456852222347,"score_spread":0.06097613576928418,"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."}}