{"id":"W3022938419","doi":"10.2196/15407","title":"Detecting False Alarms by Analyzing Alarm-Context Information: Algorithm Development and Validation","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"ALARM; Computer science; Context (archaeology); False alarm; Patient safety; Health care; Medical emergency; Computer security; Machine learning; Medicine","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":[0.008060043,0.001742346,0.001239988,0.002086728,0.0007245654,0.002180207,0.003211106,0.002388315,0.001903044],"category_scores_gemma":[0.03195057,0.0006395972,0.001071921,0.001125645,0.0008021183,0.00194171,0.00154072,0.001950651,0.0008954842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001254165,"about_ca_system_score_gemma":0.003897216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007943279,"about_ca_topic_score_gemma":0.004183902,"domain_scores_codex":[0.995087,0.001707826,0.0005719397,0.001113871,0.001229192,0.0002903197],"domain_scores_gemma":[0.977527,0.01565128,0.0008962355,0.001336505,0.004347855,0.0002411229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000896765,0.001138087,0.02460111,0.0006193969,0.0004271956,0.0002352622,0.0003537914,0.2960469,0.01293062,0.003578765,0.003555166,0.655617],"study_design_scores_gemma":[0.00007938645,0.0001148515,0.001098339,0.00003698656,0.0000465096,0.00009295177,0.00003779888,0.9903861,0.00626105,0.001091039,0.0007391076,0.00001594524],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0411273,0.0004344864,0.9492193,0.0002543578,0.00005945434,0.0007057126,0.0002351918,0.007253836,0.0007104138],"genre_scores_gemma":[0.1935456,0.0002009053,0.8042391,0.0001533604,0.0000248953,0.0007848865,0.0005397322,0.0001311816,0.000380317],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008060043,"threshold_uncertainty_score":0.04262608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02960060390083487,"score_gpt":0.3092137435588035,"score_spread":0.2796131396579686,"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."}}