{"id":"W2101013891","doi":"10.1109/iembs.2005.1616531","title":"Adaptive Change Point Detection for Respiratory Variables","year":2005,"lang":"en","type":"article","venue":"","topic":"Hemodynamic Monitoring and Therapy","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Children's Hospital; University of British Columbia","funders":"","keywords":"CUSUM; Computer science; Change detection; Kalman filter; Noise (video); Adaptive filter; Constant false alarm rate; Maximization; Algorithm; Artificial intelligence; Statistics; Mathematics; Mathematical optimization","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001652115,0.0006082449,0.0006453109,0.0010123,0.0002875422,0.0005142015,0.0008926262,0.000873272,0.0008985839],"category_scores_gemma":[0.01003507,0.0002837603,0.0004831333,0.0005407966,0.0005839618,0.0006414237,0.0006750241,0.0008918594,0.0003139575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005452823,"about_ca_system_score_gemma":0.0006118999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003614217,"about_ca_topic_score_gemma":0.002443123,"domain_scores_codex":[0.9987116,0.0003673026,0.00007173636,0.000390416,0.0003643792,0.00009443281],"domain_scores_gemma":[0.9952548,0.003305731,0.0003848613,0.0003184298,0.0006483874,0.00008778417],"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":[0.0006758715,0.0002279543,0.01290965,0.000144935,0.0001201164,0.0003269239,0.0003015201,0.2970998,0.06786831,0.006079405,0.001875082,0.6123704],"study_design_scores_gemma":[0.00001475953,0.00007543668,0.00229691,0.000004213005,0.00001087566,0.0001123844,0.000009509286,0.9866811,0.009062284,0.001150933,0.0005612765,0.00002031023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02945834,0.0001104712,0.9691604,0.00006996901,0.00003393194,0.00004651279,0.00003206236,0.0008165916,0.0002716191],"genre_scores_gemma":[0.6233361,0.0001159233,0.3749386,0.00009178984,0.00005603354,0.0001612299,0.0001288092,0.00009790005,0.001073448],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003614217,"threshold_uncertainty_score":0.008737385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06852656218711076,"score_gpt":0.3127075841360499,"score_spread":0.2441810219489391,"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."}}