{"id":"W4402660105","doi":"10.1109/mwscas60917.2024.10658743","title":"Cough Event Prediction based on Spectral Features and SVM and KNN Machine learning using Triaxial Accelerometer data from Multiple body positions","year":2024,"lang":"en","type":"article","venue":"","topic":"Respiratory and Cough-Related Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Accelerometer; Support vector machine; Computer science; Artificial intelligence; Pattern recognition (psychology); Event (particle physics); Machine learning; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004028766,0.0006313972,0.0006701991,0.001052255,0.0002318524,0.0004958553,0.0003809261,0.0005827753,0.0008772028],"category_scores_gemma":[0.001318389,0.0001744102,0.0004656319,0.0006920769,0.0001470141,0.0005408674,0.0003517468,0.0004562972,0.0004411974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002622143,"about_ca_system_score_gemma":0.0002922119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00290536,"about_ca_topic_score_gemma":0.003702108,"domain_scores_codex":[0.9997228,0.00004104708,0.00002737258,0.00007286834,0.00008508535,0.00005077666],"domain_scores_gemma":[0.999494,0.0002055904,0.00008345334,0.00003186577,0.000154724,0.00003038926],"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":[0.00094883,0.0004447022,0.038111,0.000203104,0.0001982022,0.0004212263,0.0001117904,0.1044903,0.03896374,0.0005101979,0.002337025,0.81326],"study_design_scores_gemma":[0.000008302181,0.000187927,0.01676203,0.00002015919,0.00003094307,0.0001296162,0.00006649518,0.9757174,0.0063156,0.0003311805,0.0004155022,0.00001500515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4692337,0.001339209,0.5239778,0.0002381526,0.0002645455,0.0001096022,0.0005705753,0.001443906,0.002822446],"genre_scores_gemma":[0.9408439,0.0002731133,0.05667605,0.00003215007,0.00006579408,0.00004921061,0.0004174931,0.00001991974,0.001622229],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00290536,"threshold_uncertainty_score":0.005776942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07557433078255979,"score_gpt":0.3533479188519853,"score_spread":0.2777735880694255,"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."}}