{"id":"W1995606590","doi":"10.5539/cis.v3n3p197","title":"Anomaly Detection of Clinical Behavior Sequences","year":2010,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Subsequence; Computer science; Anomaly detection; Similarity (geometry); Association rule learning; Anomaly (physics); Sequence (biology); Identification (biology); Pattern recognition (psychology); Data mining; Process (computing); Base (topology); Artificial intelligence; Algorithm; Mathematics; Biology","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":[0.0009343493,0.00005077628,0.00008522888,0.0001444689,0.0001796838,0.0002809535,0.0006831215,0.00003143851,0.000002195147],"category_scores_gemma":[0.00004624949,0.00004221783,0.00002139961,0.000568322,0.0003762585,0.007367082,0.0002496093,0.0001097246,0.00001598239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003095404,"about_ca_system_score_gemma":0.00009973632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001795123,"about_ca_topic_score_gemma":0.000002146063,"domain_scores_codex":[0.9991354,0.0000081717,0.0003674034,0.0001511404,0.0002306182,0.0001073148],"domain_scores_gemma":[0.9991725,0.00004421548,0.0001627951,0.0003240278,0.0002073663,0.00008906758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[3.478692e-7,0.00001326395,0.001828582,0.000002618826,5.740604e-7,8.370292e-8,0.0002065688,0.000002458556,0.001916304,0.02299113,0.00002024193,0.9730178],"study_design_scores_gemma":[0.0001625457,0.0001068911,0.4078243,0.000005057998,0.000002655048,0.00003758299,0.00001809183,0.5721189,0.006864779,0.0002197716,0.01252976,0.0001095818],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3736844,0.000001970359,0.6249682,0.00005985367,0.0005477521,0.00006248694,0.000002881276,0.00003704103,0.0006354072],"genre_scores_gemma":[0.8149027,0.000006305214,0.1849099,0.0001259892,0.00004309796,0.000007300141,0.000001533333,5.750802e-7,0.000002639647],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9729083,"threshold_uncertainty_score":0.5340957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02657666134097324,"score_gpt":0.3295680344353278,"score_spread":0.3029913730943546,"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."}}