{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001283532,0.0004649592,0.0006135763,0.00392757,0.0003824459,0.0007303927,0.0007142543,0.0005591038,0.0007489085],"category_scores_gemma":[0.009607034,0.000173613,0.0004093885,0.002221677,0.0003789091,0.0007793847,0.0005245221,0.0004621062,0.0002485771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004123368,"about_ca_system_score_gemma":0.001056647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002291028,"about_ca_topic_score_gemma":0.001615557,"domain_scores_codex":[0.998255,0.0003301461,0.000252345,0.0004554812,0.0006015776,0.0001054981],"domain_scores_gemma":[0.9947857,0.002473942,0.0008358429,0.0004009328,0.001251774,0.000251854],"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":[0.001372358,0.0004360757,0.2530044,0.0005333076,0.000295186,0.003779965,0.001038365,0.04016509,0.04847364,0.009933872,0.00321845,0.6377494],"study_design_scores_gemma":[0.00008413538,0.0007903607,0.08741665,0.00009094419,0.0002316917,0.007665349,0.0005791727,0.8275685,0.04191678,0.02619088,0.007363091,0.000102379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4675562,0.0007082966,0.5250982,0.0004702431,0.0001169211,0.000321078,0.001547791,0.002159432,0.002021912],"genre_scores_gemma":[0.8674001,0.0002547564,0.129825,0.00006007181,0.00004302959,0.000128065,0.001592306,0.00003390373,0.000662801],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00392757,"threshold_uncertainty_score":0.006788015,"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."}}