{"id":"W4288766932","doi":"10.2196/38211","title":"An Unsupervised Data-Driven Anomaly Detection Approach for Adverse Health Conditions in People Living With Dementia: Cohort Study","year":2022,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Anomaly detection; Dementia; Outlier; Computer science; Univariate; Generalizability theory; Multivariate statistics; Artificial intelligence; Data mining; Statistics; Medicine; Machine learning; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.001305195,0.000146802,0.0002716143,0.0003119601,0.0006864262,0.0001308824,0.0008145954,0.00001783103,0.00002965937],"category_scores_gemma":[0.00001340416,0.0001621352,0.00003341428,0.0007761343,0.00001329514,0.001441353,0.0004777525,0.0002060577,0.000001978301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002629883,"about_ca_system_score_gemma":0.0001906216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001169237,"about_ca_topic_score_gemma":0.004121005,"domain_scores_codex":[0.9976701,0.0005290402,0.0003138527,0.0007905572,0.0003767085,0.0003197926],"domain_scores_gemma":[0.9985892,0.000152983,0.0001890613,0.0009114486,0.00006513644,0.00009213486],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001265734,0.001307541,0.9768047,0.00006401668,0.0001064361,0.000006044175,0.008793917,0.004288215,0.000127916,0.00002173685,0.0000737895,0.00839302],"study_design_scores_gemma":[0.0009027417,0.0004387884,0.5728984,0.00002157817,0.00001779346,0.00003316019,0.00857808,0.4167899,0.000004314528,0.000004302153,0.0001155187,0.0001953519],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5438245,0.0000113158,0.4533007,0.0000680978,0.0001007367,0.002406425,0.00004731791,0.0001702664,0.00007058074],"genre_scores_gemma":[0.9931998,4.666277e-7,0.003706562,0.0001297165,0.00004157034,0.002727571,0.0001563492,0.00002012273,0.00001790186],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4495942,"threshold_uncertainty_score":0.6611682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03617638418028334,"score_gpt":0.3049046729364046,"score_spread":0.2687282887561213,"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."}}