{"id":"W2885506102","doi":"10.1371/journal.pone.0222212","title":"Using path signatures to predict a diagnosis of Alzheimer’s disease","year":2019,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Alzheimer's disease research and treatments","field":"Medicine","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; Dementias Platform UK; IXICO; H. Lundbeck A/S; Servier; Eisai; F. Hoffmann-La Roche; Meso Scale Diagnostics; Medical Research Council; Pfizer; Biogen; BioClinica; European Commission; Eli Lilly and Company; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Engineering and Physical Sciences Research Council; Bristol-Myers Squibb; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Neuroimaging; Computer science; Path (computing); Artificial intelligence; Signature (topology); Gold standard (test); Pattern recognition (psychology); Hippocampal formation; Alzheimer's Disease Neuroimaging Initiative; Hippocampus; Feature selection; Alzheimer's disease; Neuroscience; Disease; Medicine; Psychology; Mathematics; Pathology; Internal medicine","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":[0.001284591,0.0005762478,0.0003797949,0.002408923,0.0001949179,0.0007172631,0.0003075964,0.0006281507,0.00223183],"category_scores_gemma":[0.007573255,0.0001389884,0.0004254265,0.001107978,0.0002523354,0.0007425797,0.000575476,0.0006257112,0.0006784917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002780071,"about_ca_system_score_gemma":0.0006167041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002699123,"about_ca_topic_score_gemma":0.002443607,"domain_scores_codex":[0.9995975,0.000141704,0.00003882824,0.00009077875,0.00008015905,0.00005102114],"domain_scores_gemma":[0.9963676,0.002062931,0.0007739884,0.0002275125,0.0003770831,0.0001909011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001845235,0.0002370082,0.7741157,0.00008835207,0.0002625111,0.0005236774,0.0001390136,0.01859085,0.006672808,0.001267892,0.002759868,0.1934971],"study_design_scores_gemma":[0.0001468343,0.001019813,0.4813803,0.00007418512,0.0002390406,0.00241854,0.0003672449,0.48397,0.009007839,0.01738106,0.00390063,0.00009436812],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9333721,0.0005442792,0.05939216,0.0005628971,0.0000988295,0.00009057073,0.003350469,0.0006237127,0.001965057],"genre_scores_gemma":[0.9762896,0.0001517194,0.02124807,0.00003969381,0.00003663285,0.00003539921,0.001732208,0.00002041354,0.0004463427],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002699123,"threshold_uncertainty_score":0.007466257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08992353972328308,"score_gpt":0.3209466136347883,"score_spread":0.2310230739115052,"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."}}